Tootfinder

Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods
Min Kyu Shin, Su-Jeong Park, Seung-Keol Ryu, Heeyeon Kim, Han-Lim Choi
https://arxiv.org/abs/2404.16721

Distilling Privileged Information for Dubins Traveling Salesman Problems with Neighborhoods
This paper presents a novel learning approach for Dubins Traveling Salesman Problems(DTSP) with Neighborhood (DTSPN) to quickly produce a tour of a non-holonomic vehicle passing through neighborhoods of given task points. The method involves two learning phases: initially, a model-free reinforcement learning approach leverages privileged information to distill knowledge from expert trajectories generated by the LinKernighan heuristic (LKH) algorithm. Subsequently, a supervised learning phase tr…

Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on a Dexterous Robotic Hand
Davide Liconti, Yasunori Toshimitsu, Robert Katzschmann
https://arxiv.org/abs/2404.16483

Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on a Dexterous Robotic Hand
In the context of imitation learning applied to dexterous robotic hands, the high complexity of the systems makes learning complex manipulation tasks challenging. However, the numerous datasets depicting human hands in various different tasks could provide us with better knowledge regarding human hand motion. We propose a method to leverage multiple large-scale task-agnostic datasets to obtain latent representations that effectively encode motion subtrajectories that we included in a transforme…

If you’re a [physics] theorist interested in getting funding, obviously the thing to do was to pivot quickly from quantum computing to machine learning and AI,
and get to work on the people at Quanta [magazine] to provide suitable PR.
Today Quanta features an article explaining how “Using machine learning, string theorists are finally showing how microscopic configurations of extra dimensions translate into sets of elementary particles.”
Looking at these new neural network c…

Adaptive Learning-based Model Predictive Control for Uncertain Interconnected Systems: A Set Membership Identification Approach
Ahmed Aboudonia, John Lygeros
https://arxiv.org/abs/2404.16514

Adaptive Learning-based Model Predictive Control for Uncertain Interconnected Systems: A Set Membership Identification Approach
We propose a novel adaptive learning-based model predictive control (MPC) scheme for interconnected systems which can be decomposed into several smaller dynamically coupled subsystems with uncertain coupling. The proposed scheme is mainly divided into two main online phases; a learning phase and an adaptation phase. Set membership identification is used in the learning phase to learn an uncertainty set that contains the coupling strength using online data. In the adaptation phase, rigid tube-ba…

This https://arxiv.org/abs/2404.10536 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Benchmarking Machine Learning Applications on Heterogeneous Architecture using Reframe
With the rapid increase in machine learning workloads performed on HPC systems, it is beneficial to regularly perform machine learning specific benchmarks to monitor performance and identify issues. Furthermore, as part of the Edinburgh International Data Facility, EPCC currently hosts a wide range of machine learning accelerators including Nvidia GPUs, the Graphcore Bow Pod64 and Cerebras CS-2, which are managed via Kubernetes and Slurm. We extended the Reframe framework to support the Kuberne…

Praktika, which lets users create personalized AI-powered avatars to make learning a language feel more natural, raised a $35.5M Series A led by Blossom Capital (Mike Butcher/TechCrunch)
https://techcrunch.com/2024/05/22/prak

Praktika raises $35.5M to use AI avatars to make learning languages feel more natural | TechCrunch
Praktika has secured a $35.5M Series A round to apply AI-powered avatars to language-learning apps.

This short essay by @… is spot on. He talks about the need to seek out those who disagree with you, a cornerstone to learning and growing, and
The Columbia faculty along with those of Yale, NYU, and other campuses now engulfed in protests against what is occurring in Gaza should do everything in their power to use the resulting provocations, inconveniences, and discomforts as occasions for learning rather than repression.
https://robertreich.substack.com/p/the-most-important-thing-i-teach-555

Also, today's good code is likely tomorrow's bad code.
https://mastodon.social/@dabeaz/112507379669620422

Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference
Zhe Zhang, Ryumei Nakada, Linjun Zhang
https://arxiv.org/abs/2404.16287 <…

Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference
Differentially private federated learning is crucial for maintaining privacy in distributed environments. This paper investigates the challenges of high-dimensional estimation and inference under the constraints of differential privacy. First, we study scenarios involving an untrusted central server, demonstrating the inherent difficulties of accurate estimation in high-dimensional problems. Our findings indicate that the tight minimax rates depends on the high-dimensionality of the data even w…

A Short Review for Ontology Learning from Text: Stride from Shallow Learning, Deep Learning to Large Language Models Trend
Rick Du, Huilong An, Keyu Wang, Weidong Liu
https://arxiv.org/abs/2404.14991

A Short Review for Ontology Learning from Text: Stride from Shallow Learning, Deep Learning to Large Language Models Trend
Ontologies provide formal representation of knowledge shared within Semantic Web applications and Ontology learning from text involves the construction of ontologies from a given corpus of text. In the past years, ontology learning has traversed through shallow learning and deep learning methodologies, each offering distinct advantages and limitations in the quest for knowledge extraction and representation. A new trend of these approaches is relying on large language models to enhance ontology…

This https://arxiv.org/abs/2404.15736 has been replaced.
link: https://scholar.google.com/scholar?q=a

What Makes Multimodal In-Context Learning Work?
Large Language Models have demonstrated remarkable performance across various tasks, exhibiting the capacity to swiftly acquire new skills, such as through In-Context Learning (ICL) with minimal demonstration examples. In this work, we present a comprehensive framework for investigating Multimodal ICL (M-ICL) in the context of Large Multimodal Models. We consider the best open-source multimodal models (e.g., IDEFICS, OpenFlamingo) and a wide range of multimodal tasks. Our study unveils several …

Ottimizza il Tuo Codice di Machine Learning con MLflow e Hydra: Un Guida Completa
https://poliverso.org/display/0477a01e-04bfffd1-572e5f7a8ffac93e
Ottimizza il Tuo Codice di Machine Learning con MLflow e Hydra: Un Guida Completa Quando sviluppiamo m…

Improved impedance inversion by deep learning and iterated graph Laplacian
Davide Bianchi, Florian Bossmann, Wenlong Wang, Mingming Liu
https://arxiv.org/abs/2404.16324

Improved impedance inversion by deep learning and iterated graph Laplacian
Deep learning techniques have shown significant potential in many applications through recent years. The achieved results often outperform traditional techniques. However, the quality of a neural network highly depends on the used training data. Noisy, insufficient, or biased training data leads to suboptimal results. We present a hybrid method that combines deep learning with iterated graph Laplacian and show its application in acoustic impedance inversion which is a routine procedure in sei…

Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on a Dexterous Robotic Hand
Davide Liconti, Yasunori Toshimitsu, Robert Katzschmann
https://arxiv.org/abs/2404.16483

Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on a Dexterous Robotic Hand
In the context of imitation learning applied to dexterous robotic hands, the high complexity of the systems makes learning complex manipulation tasks challenging. However, the numerous datasets depicting human hands in various different tasks could provide us with better knowledge regarding human hand motion. We propose a method to leverage multiple large-scale task-agnostic datasets to obtain latent representations that effectively encode motion subtrajectories that we included in a transforme…

When I was young, ages 13-15 were "just learning to be an adult"
now we have pushed the"just learning to be an adult" phase up to ages 18-22.
It's extremely unhealthy for our teenagers.

When Generative AI Meets Workplace Learning: Creating A Realistic & Motivating Learning Experience With A Generative PCA
Andreas Bucher, Birgit Schenk, Mateusz Dolata, Gerhard Schwabe
https://arxiv.org/abs/2405.15561

Google has a new machine learning gibberish generator but it looks like my adblocker scuppers it.
I tried a search for steady state Manchester on Google, Mojeek, Qwant, and Duckduckgo .
All results were relevant but a bit odd - lots of historical stuff. The bing based qwant and duck did best on this trial.

Dynamically Anchored Prompting for Task-Imbalanced Continual Learning
Chenxing Hong, Yan Jin, Zhiqi Kang, Yizhou Chen, Mengke Li, Yang Lu, Hanzi Wang
https://arxiv.org/abs/2404.14721

Dynamically Anchored Prompting for Task-Imbalanced Continual Learning
Existing continual learning literature relies heavily on a strong assumption that tasks arrive with a balanced data stream, which is often unrealistic in real-world applications. In this work, we explore task-imbalanced continual learning (TICL) scenarios where the distribution of task data is non-uniform across the whole learning process. We find that imbalanced tasks significantly challenge the capability of models to control the trade-off between stability and plasticity from the perspective…

FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks
Bingnan Xiao, Jingjing Zhang, Wei Ni, Xin Wang
https://arxiv.org/abs/2404.14811

FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks
Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper presents a new Federated Learning with Adjusted leaRning ratE (FLARE) framework to mitigate the impact of the heterogeneity. The key idea is to allow the participating devices to adjust their individual learning rates and local training iterations, adapting to their instantaneous computing powers. The convergence upper b…

Read to your babies

As climate change raises school temperatures, some are too hot for learning - Washington Post
https://www.washingtonpost.com/climate-environment/interactive/2024/school-temperatures-heat-climate-change/

As climate change raises school temperatures, some are too hot for learning
As heat waves creep north, they are baking schools that previously did not need air conditioning. Fixing the problem will be neither cheap, nor easy.

This https://arxiv.org/abs/2304.00083 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service Inference
The growing popularity of Machine Learning (ML) has led to its deployment in various sensitive domains, which has resulted in significant research focused on ML security and privacy. However, in some applications, such as Augmented/Virtual Reality, integrity verification of the outsourced ML tasks is more critical--a facet that has not received much attention. Existing solutions, such as multi-party computation and proof-based systems, impose significant computation overhead, which makes them u…

Marco Klindt von @… fängt bei der Erläuterung von Machine Learning einfach an 🙂

Learning World Models With Hierarchical Temporal Abstractions: A Probabilistic Perspective
Vaisakh Shaj
https://arxiv.org/abs/2404.16078 https://

Learning World Models With Hierarchical Temporal Abstractions: A Probabilistic Perspective
Machines that can replicate human intelligence with type 2 reasoning capabilities should be able to reason at multiple levels of spatio-temporal abstractions and scales using internal world models. Devising formalisms to develop such internal world models, which accurately reflect the causal hierarchies inherent in the dynamics of the real world, is a critical research challenge in the domains of artificial intelligence and machine learning. This thesis identifies several limitations with the p…

If you are learning Python, I'm making a clicker-style game (but that doesn't threaten the lifespan of your mouse) at 1400 BST (1300 UTC).
Live coding: Cookie clicker, but without clicking (or cookies) #python #...
https://

Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
Ben Williams, Bart van Merri\"enboer, Vincent Dumoulin, Jenny Hamer, Eleni Triantafillou, Abram B. Fleishman, Matthew McKown, Jill E. Munger, Aaron N. Rice, Ashlee Lillis, Clemency E. White, Catherine A. D. Hobbs, Tries B. Razak, Kate E. Jones, Tom Denton

Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's efficacy. Generalizable pretrained networks can overcome these costs, but high-quality pretraining requires vast annotated libraries, limiting its current applicability primarily to bird taxa. Here, we identify the optimum pretraining strategy for a data-deficient domain using coral reef bioacoustics. We assemble ReefSet, …

This https://arxiv.org/abs/2302.14648 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Digital Over-the-Air Federated Learning in Multi-Antenna Systems
In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The P…

Chorna on Ukrainian Representation From Tokenism to Mutual Learning in Museums: https://benborges.xyz/2024/05/24/chorna-on-ukrainian.html

Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds
Minwu Kim
https://arxiv.org/abs/2404.16169 https://

Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds
This work develops a predictive model to identify potential targets of activist investment funds, which strategically acquire significant corporate stakes to drive operational and strategic improvements and enhance shareholder value. Predicting these targets is crucial for companies to mitigate intervention risks, for activists to select optimal targets, and for investors to capitalize on associated stock price gains. Our analysis utilizes data from the Russell 3000 index from 2016 to 2022. We …

Re-upping this from April, for everyone in #SouthAfrica, with polls opening in less than two days: It is imperative that you vote ANC if you care about foreign correspondents.
* Foreign journalists don't need the stress of learning to spell a new President's name.
* Foreign desks don't need the stress of coalition-government drama.
* Nobody likes fresh ideas and vigour when everything is going... Well, it's still going, well.
https://www.news24.com/news24/opinions/columnists/phillipdewet/phillip-de-wet-on-behalf-of-our-foreign-colleagues-please-just-vote-anc-20240426

Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge
Kendall Schmidt (American College of Radiology, USA), Benjamin Bearce (The Massachusetts General Hospital, USA,University of Colorado, USA), Ken Chang (The Massachusetts General Hospital), Laura Coombs (American College of Radiology, USA), Keyvan Farahani (National Institutes of Health National Cancer Institute, USA), Marawan …

Buyima Gathang
Great Australian Pods Podcast Directory: #GreatAusPods

Buyima Gathang | Great Australian Pods Podcast Directory
Podcast: Buyima Gathang Host: Jazlie Davis Category: Education Sub-Category: Culture & Identity, Language Learning & Diversity First Episode: 2024 Buyima Gathang is a podcast about Gathang Language.

Experience transforms crossmodal object representations in the anterior temporal lobes #Crossmodal binding problem, crossmodal learning, learned cross…

Experience transforms crossmodal object representations in the anterior temporal lobes

SlackあたりがAIの利用規約を丁寧に説明してるのと比べてしまうと、アルトマンの行動はいずれ許されないものになってくる気がするよ。
https://slack.com/blog/news/how-slack-protects-your-data-when-using-machine-learning-and-ai

How Slack protects your data when using machine learning and AI
Slack is where work flows. It's where the people you need, the information you share, and the tools you use come together to get things done.

"""
The case of One Laptop per Child shows us why it is dangerous to ignore the origins of charisma: one risks being perpetually entranced by the newest charismatic technology. This is not to say that cultural change with a technology-centric project is impossible. Still, even more realistic reforms grounded in the realities of their intended beneficiaries sometimes have difficulty gaining broad popular support outside the school unless they add a charismatic gloss of rapid, revolutionary change.
This charismatic pressure can put even open-eyed reformers in a catch-22. They must promise dramatic results to gain the social and financial support for reforms, and then they must either admit to not achieving their goals or pretend that they did achieve them. Either way, funders will declare that the project is finished and withdraw financial support, and then researchers and other observers will begin to note the discrepancies between reformers’ promises and their own observations. Thus, projects that rely on charismatic technologies are often short lived; their resources are cut off before charisma recedes into the background and before the technology becomes part of everyday classroom experience. This catch-22 has dogged efforts for educational reform, development, and cultural change — especially those funded through grants or other short-term funding — for well over a century. As the technology community moves on to the next charismatic device without learning from its failures, this will continue to hamper the possibility of real, if incremental, change.
[…] After all, charisma is ultimately a conservative social force. Even when charismatic technologies promise to quickly and painlessly transform our lives for the better, they appeal precisely because they echo existing stereotypes, confirm the value of existing power relations, and reinforce existing ideologies. Meanwhile, they may divert attention and resources from more complicated, expensive, or politically charged reforms that do not promise a quick fix and are thus less charismatic.
"""
(Morgan G. Ames, The Charisma Machine)

FLAASH: Flexible Accelerator Architecture for Sparse High-Order Tensor Contraction
Gabriel Kulp, Andrew Ensinger, Lizhong Chen
https://arxiv.org/abs/2404.16317

FLAASH: Flexible Accelerator Architecture for Sparse High-Order Tensor Contraction
Tensors play a vital role in machine learning (ML) and often exhibit properties best explored while maintaining high-order. Efficiently performing ML computations requires taking advantage of sparsity, but generalized hardware support is challenging. This paper introduces FLAASH, a flexible and modular accelerator design for sparse tensor contraction that achieves over 25x speedup for a deep learning workload. Our architecture performs sparse high-order tensor contraction by distributing sparse…

Bisimulation Learning
Alessandro Abate, Mirco Giacobbe, Yannik Schnitzer
https://arxiv.org/abs/2405.15723 https://arxiv.org/pdf/2405.…

Very nice picture that was shared by Ronald van Loon on X, you can discuss if the categories are complete and correct, but it illustrates that the field of AI is much more then just transformers/LLMs.
#AI #Machinelearning

OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks
Vinicius Mikuni, Benjamin Nachman
https://arxiv.org/abs/2404.16091 https://

OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks
Machine learning has become an essential tool in jet physics. Due to their complex, high-dimensional nature, jets can be explored holistically by neural networks in ways that are not possible manually. However, innovations in all areas of jet physics are proceeding in parallel. We show that specially constructed machine learning models trained for a specific jet classification task can improve the accuracy, precision, or speed of all other jet physics tasks. This is demonstrated by training on …

"The safety of all road users is a top priority for Waymo, and we look forward to learning from this unique event," says company whose defective robotaxi drove two blocks at fairly high speed on the wrong side of the road. They could've killed someone and it's a fun learning opportunity for them. #Robotaxis suck. Cone them.

Filings: US pharmaceutical company Cencora notified ~500K individuals since learning about a data breach in February 2024 that exposed health diagnoses and more (Zack Whittaker/TechCrunch)
https://techcrunch.com/2024/05/24/cencora-america…

US drug maker Cencora says Americans' health information stolen in data breach | TechCrunch
About half a million patients have been notified so far, but the number of affected individuals is likely far higher.

Enhancing Quality of Experience in Telecommunication Networks: A Review of Frameworks and Machine Learning Algorithms
Parsa H. S. Panahi, Amir H. Jalilvand, Abolfazl Diyanat
https://arxiv.org/abs/2404.16787

Enhancing Quality of Experience in Telecommunication Networks: A Review of Frameworks and Machine Learning Algorithms
The Internet service provider industry is currently experiencing intense competition as companies strive to provide top-notch services to their customers. Providers are introducing cutting-edge technologies to enhance service quality, understanding that their survival depends on the level of service they offer. However, evaluating service quality is a complex task. A crucial aspect of this evaluation lies in understanding user experience, which significantly impacts the success and reputation o…

This https://arxiv.org/abs/2307.16811 has been replaced.
link: https://scholar.google.com/scholar?q=a

DoDo Learning: DOmain-DemOgraphic Transfer in Language Models for Detecting Abuse Targeted at Public Figures
Public figures receive a disproportionate amount of abuse on social media, impacting their active participation in public life. Automated systems can identify abuse at scale but labelling training data is expensive, complex and potentially harmful. So, it is desirable that systems are efficient and generalisable, handling both shared and specific aspects of online abuse. We explore the dynamics of cross-group text classification in order to understand how well classifiers trained on one domain …

This https://arxiv.org/abs/2404.15419 has been replaced.
link: https://scholar.google.com/scholar?q=a

Using Deep Learning to Identify Initial Error Sensitivity of ENSO Forecasts
We introduce a hybrid method that integrates deep learning with model-analog forecasting, a straightforward yet effective approach that generates forecasts from similar initial climate states in a repository of model simulations. This hybrid framework employs a convolutional neural network to estimate state-dependent weights to identify analog states. The advantage of our method lies in its physical interpretability, offering insights into initial-error-sensitive regions through estimated weigh…

Evolutionary Reinforcement Learning via Cooperative Coevolution
Chengpeng Hu, Jialin Liu, Xin Yao
https://arxiv.org/abs/2404.14763 https://

Evolutionary Reinforcement Learning via Cooperative Coevolution
Recently, evolutionary reinforcement learning has obtained much attention in various domains. Maintaining a population of actors, evolutionary reinforcement learning utilises the collected experiences to improve the behaviour policy through efficient exploration. However, the poor scalability of genetic operators limits the efficiency of optimising high-dimensional neural networks. To address this issue, this paper proposes a novel cooperative coevolutionary reinforcement learning (CoERL) algor…

Eagles' Darius Slay already taking Quinyon Mitchell under his wing; how rookie CB has impressed during OTAs

https://www.cbssports.com/nfl/ne…

Eagles' Darius Slay already taking Quinyon Mitchell under his wing; how rookie CB has impressed during OTAs
Mitchell is learning from Slay to start his NFL career

Daughter and I got election materials from Valencia today. I guess for the European election?! More learning required!
#Europe #España #Spain

One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns
Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir G. Kim, Matthew Fisher, S\"oren Pirk, Daniel Ritchie
https://arxiv.org/abs/2404.16292

One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns
Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In this paper, we present a single generative model which can learn to generate multiple types of noise as well as blend between them. In addition, it is capable of producing spatially-varying noise blends despite not having access to such data for training. Th…

Learning Visuotactile Skills with Two Multifingered Hands
Toru Lin, Yu Zhang, Qiyang Li, Haozhi Qi, Brent Yi, Sergey Levine, Jitendra Malik
https://arxiv.org/abs/2404.16823

Learning Visuotactile Skills with Two Multifingered Hands
Aiming to replicate human-like dexterity, perceptual experiences, and motion patterns, we explore learning from human demonstrations using a bimanual system with multifingered hands and visuotactile data. Two significant challenges exist: the lack of an affordable and accessible teleoperation system suitable for a dual-arm setup with multifingered hands, and the scarcity of multifingered hand hardware equipped with touch sensing. To tackle the first challenge, we develop HATO, a low-cost hands-…

Vector databases give you the infrastructure to store the embeddings, but how those embeddings are made is the most innovative part of all. Join Sonam Pankaj at this year's Berlin Buzzwords for her short talk on metric learning. #bbuzz
https…

This https://arxiv.org/abs/2404.15527 has been replaced.
link: https://scholar.google.com/scholar?q=a

NGC1856: Using machine learning techniques to uncover detailed stellar abundances from MUSE data
We present the first application of the novel approach based on data-driven machine learning methods applied to Multi-Unit Spectroscopic Explorer (MUSE) field data to derive stellar abundances of star clusters. MUSE has been used to target more than 10,000 fields, and it is unique in its ability to study dense stellar fields such as stellar clusters providing spectra for each individual star. We use MUSE data of the extragalactic young stellar cluster NGC 1856, located in the Large Magellanic C…

When Generative AI Meets Workplace Learning: Creating A Realistic & Motivating Learning Experience With A Generative PCA
Andreas Bucher, Birgit Schenk, Mateusz Dolata, Gerhard Schwabe
https://arxiv.org/abs/2405.15561

How we Learn Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching
Zhong Wang
https://arxiv.org/abs/2404.14867 https://

How we Learn Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching
This article reviews the psychological and neuroscience achievements in concept learning since 2010 from the perspectives of individual learning and social learning, and discusses several issues related to concept learning, including the assistance of machine learning about concept learning. 1 In terms of individual learning, current evidences shown that the brain tends to process concrete concepts through typical features (shared features); And abstract concepts, semantic processing is the mos…

Google has a new machine learning gibberish generator but it looks like my adblocker scuppers it.
I tried a search for steady state Manchester on Google, Mojeek, Qwant, and Duckduckgo .
All results were relevant but a bit odd - lots of historical stuff. The bing based qwant and duck did best on this trial.

This kind of brain damage goes by many names...
Learning. Growing up. Maturing. Existing.
I'm not always against ALL of them.
Yesterday was a bad day. Sorry.
#Physiology #Philosophy of #Brains and…

Robert Reich:
Protesting against slaughter – as students in the US are doing – isn’t antisemitism
The most important thing I teach my students is to seek out people who disagree with them.
That’s because the essence of learning is testing one’s ideas, assumptions and values.
And what better place to test ideas, assumptions and values than at a university?
Apparently, Columbia University’s president, Minouche Shafik, does not share my view.
Last week she p…

This https://arxiv.org/abs/2404.08233 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement Learning
Hyperparameter optimization plays a key role in the machine learning domain. Its significance is especially pronounced in reinforcement learning (RL), where agents continuously interact with and adapt to their environments, requiring dynamic adjustments in their learning trajectories. To cater to this dynamicity, the Population-Based Training (PBT) was introduced, leveraging the collective intelligence of a population of agents learning simultaneously. However, PBT tends to favor high-performin…

Learning to Beat ByteRL: Exploitability of Collectible Card Game Agents
Radovan Haluska, Martin Schmid
https://arxiv.org/abs/2404.16689 https://

Learning to Beat ByteRL: Exploitability of Collectible Card Game Agents
While Poker, as a family of games, has been studied extensively in the last decades, collectible card games have seen relatively little attention. Only recently have we seen an agent that can compete with professional human players in Hearthstone, one of the most popular collectible card games. Although artificial agents must be able to work with imperfect information in both of these genres, collectible card games pose another set of distinct challenges. Unlike in many poker variants, agents m…

The way people anthropomorphise deep learning models, it's like bringing back the Participation Mystique of pre-agrarian cultures, but we get to use as much power as a small nation doing it.

This https://arxiv.org/abs/2404.09779 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A replica analysis of under-bagging
A sharp asymptotics of the under-bagging (UB) method, which is a popular ensemble learning method for training classifiers from an imbalanced data, is derived and used to compare with several other standard methods for learning from imbalanced data, in the scenario where a linear classifier is trained from a binary mixture data. The methods compared include the under-sampling (US) method, which trains a model using a single realization of the subsampled dataset, and the simple weighting (SW) me…

Learning-Based Efficient Approximation of Data-enabled Predictive Control
Yihan Zhou, Yiwen Lu, Zishuo Li, Jiaqi Yan, Yilin Mo
https://arxiv.org/abs/2404.16727

Learning-Based Efficient Approximation of Data-enabled Predictive Control
Data-Enabled Predictive Control (DeePC) bypasses the need for system identification by directly leveraging raw data to formulate optimal control policies. However, the size of the optimization problem in DeePC grows linearly with respect to the data size, which prohibits its application due to high computational costs. In this paper, we propose an efficient approximation of DeePC, whose size is invariant with respect to the amount of data collected, via differentiable convex programming. Specif…

Learning Visuotactile Skills with Two Multifingered Hands
Toru Lin, Yu Zhang, Qiyang Li, Haozhi Qi, Brent Yi, Sergey Levine, Jitendra Malik
https://arxiv.org/abs/2404.16823

Learning Visuotactile Skills with Two Multifingered Hands
Aiming to replicate human-like dexterity, perceptual experiences, and motion patterns, we explore learning from human demonstrations using a bimanual system with multifingered hands and visuotactile data. Two significant challenges exist: the lack of an affordable and accessible teleoperation system suitable for a dual-arm setup with multifingered hands, and the scarcity of multifingered hand hardware equipped with touch sensing. To tackle the first challenge, we develop HATO, a low-cost hands-…

This https://arxiv.org/abs/2205.06891 has been replaced.
link: https://scholar.google.com/scholar?q=a

Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation
High-resolution (HR) magnetic resonance imaging is critical in aiding doctors in their diagnoses and image-guided treatments. However, acquiring HR images can be time-consuming and costly. Consequently, deep learning-based super-resolution reconstruction (SRR) has emerged as a promising solution for generating super-resolution (SR) images from low-resolution (LR) images. Unfortunately, training such neural networks requires aligned authentic HR and LR image pairs, which are challenging to obtai…

The Feasibility of Implementing Large-Scale Transformers on Multi-FPGA Platforms
Yu Gao, Juan Camilo Vega, Paul Chow
https://arxiv.org/abs/2404.16158 https…

The Feasibility of Implementing Large-Scale Transformers on Multi-FPGA Platforms
FPGAs are rarely mentioned when discussing the implementation of large machine learning applications, such as Large Language Models (LLMs), in the data center. There has been much evidence showing that single FPGAs can be competitive with GPUs in performance for some computations, especially for low latency, and often much more efficient when power is considered. This suggests that there is merit to exploring the use of multiple FPGAs for large machine learning applications. The challenge with …

This https://arxiv.org/abs/2404.00680 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Learning to Rank Patches for Unbiased Image Redundancy Reduction
Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However, current leading methods rely on supervisory signals. They may compel models to preserve content that aligns with labeled categories and discard content belonging to unlabeled categories. This categorical inductive bias makes these methods less effective in real-world scenarios. To ad…

RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments
Diego Martinez-Baselga, Luis Riazuelo, Luis Montano
https://arxiv.org/abs/2404.16672

RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments
Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots, with recent deep reinforcement learning approaches showing promising results. However, the complexity of the real world makes it infeasible to train agents in every possible scenario configuration. Moreover, existing methods typically overlook factors such as robot kinodynamic constraints, or assume perfect knowledge of the environment. In this work, we present RUMOR, a novel planner for differe…

This https://arxiv.org/abs/2311.03486 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Fostering Human Learning in Sequential Decision-Making: Understanding the Role of Evaluative Feedback
Cognitive rehabilitation, STEM (science, technology, engineering, and math) skill acquisition, and coaching games such as chess often require tutoring decision-making strategies. The advancement of AI-driven tutoring systems for facilitating human learning requires an understanding of the impact of evaluative feedback on human decision-making and skill development. To this end, we conduct human experiments using Amazon Mechanical Turk to study the influence of evaluative feedback on human decis…

This https://arxiv.org/abs/2404.15419 has been replaced.
link: https://scholar.google.com/scholar?q=a

Using Deep Learning to Identify Initial Error Sensitivity of ENSO Forecasts
We introduce a hybrid method that integrates deep learning with model-analog forecasting, a straightforward yet effective approach that generates forecasts from similar initial climate states in a repository of model simulations. This hybrid framework employs a convolutional neural network to estimate state-dependent weights to identify analog states. The advantage of our method lies in its physical interpretability, offering insights into initial-error-sensitive regions through estimated weigh…

This https://arxiv.org/abs/2210.02498 has been replaced.
link: https://scholar.google.com/scholar?q=a

Honest Students from Untrusted Teachers: Learning an Interpretable Question-Answering Pipeline from a Pretrained Language Model
Explainable question answering systems should produce not only accurate answers but also rationales that justify their reasoning and allow humans to check their work. But what sorts of rationales are useful and how can we train systems to produce them? We propose a new style of rationale for open-book question answering, called \emph{markup-and-mask}, which combines aspects of extractive and free-text explanations. In the markup phase, the passage is augmented with free-text markup that enables…

This https://arxiv.org/abs/2404.12633 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Gene…

This https://arxiv.org/abs/2312.09040 has been replaced.
link: https://scholar.google.com/scholar?q=a

STaR: Distilling Speech Temporal Relation for Lightweight Speech Self-Supervised Learning Models
Albeit great performance of Transformer-based speech selfsupervised learning (SSL) models, their large parameter size and computational cost make them unfavorable to utilize. In this study, we propose to compress the speech SSL models by distilling speech temporal relation (STaR). Unlike previous works that directly match the representation for each speech frame, STaR distillation transfers temporal relation between speech frames, which is more suitable for lightweight student with limited capa…

One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns
Arman Maesumi, Dylan Hu, Krishi Saripalli, Vladimir G. Kim, Matthew Fisher, S\"oren Pirk, Daniel Ritchie
https://arxiv.org/abs/2404.16292

One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns
Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In this paper, we present a single generative model which can learn to generate multiple types of noise as well as blend between them. In addition, it is capable of producing spatially-varying noise blends despite not having access to such data for training. Th…

Reinforcement Learning with Adaptive Control Regularization for Safe Control of Critical Systems
Haozhe Tian, Homayoun Hamedmoghadam, Robert Shorten, Pietro Ferraro
https://arxiv.org/abs/2404.15199

Reinforcement Learning with Adaptive Control Regularization for Safe Control of Critical Systems
Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems. Here, we propose RL with Adaptive Control Regularization (RL-ACR) that ensures RL safety by combining the RL policy with a control regularizer that hard-codes safety constraints over forecasted system behaviors. The adaptability is achieved by using a learnable "focus" weight trained to maximize the cumulati…

RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments
Diego Martinez-Baselga, Luis Riazuelo, Luis Montano
https://arxiv.org/abs/2404.16672

RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments
Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots, with recent deep reinforcement learning approaches showing promising results. However, the complexity of the real world makes it infeasible to train agents in every possible scenario configuration. Moreover, existing methods typically overlook factors such as robot kinodynamic constraints, or assume perfect knowledge of the environment. In this work, we present RUMOR, a novel planner for differe…

This https://arxiv.org/abs/2404.12633 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Gene…

This https://arxiv.org/abs/2404.13591 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning
While multi-modal large language models (MLLMs) have shown significant progress on many popular visual reasoning benchmarks, whether they possess abstract visual reasoning abilities remains an open question. Similar to the Sudoku puzzles, abstract visual reasoning (AVR) problems require finding high-level patterns (e.g., repetition constraints) that control the input shapes (e.g., digits) in a specific task configuration (e.g., matrix). However, existing AVR benchmarks only considered a limited…

Graph Machine Learning in the Era of Large Language Models (LLMs)
Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li
https://arxiv.org/abs/2404.14928

Graph Machine Learning in the Era of Large Language Models (LLMs)
Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep learning, Graph Neural Networks (GNNs) have emerged as a cornerstone in Graph Machine Learning (Graph ML), facilitating the representation and processing of graph structures. Recently, LLMs have demonstrated unprecedented capabilities in language tasks and are widely adopted in a variety of applications such as computer v…

This https://arxiv.org/abs/2401.16386 has been replaced.
link: https://scholar.google.com/scholar?q=a

Continual Learning with Pre-Trained Models: A Survey
Nowadays, real-world applications often face streaming data, which requires the learning system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve this goal and meanwhile overcome the catastrophic forgetting of former knowledge when learning new ones. Typical CL methods build the model from scratch to grow with incoming data. However, the advent of the pre-trained model (PTM) era has sparked immense research interest, particularly in leveraging PTMs' robust represe…

This https://arxiv.org/abs/2404.14560 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Adaptive Local Binary Pattern: A Novel Feature Descriptor for Enhanced Analysis of Kidney Abnormalities in CT Scan Images using ensemble based Machine Learning Approach
The shortage of nephrologists and the growing public health concern over renal failure have spurred the demand for AI systems capable of autonomously detecting kidney abnormalities. Renal failure, marked by a gradual decline in kidney function, can result from factors like cysts, stones, and tumors. Chronic kidney disease may go unnoticed initially, leading to untreated cases until they reach an advanced stage. The dataset, comprising 12,427 images from multiple hospitals in Dhaka, was categori…

This https://arxiv.org/abs/2403.19461 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Sampling Distribution and Safety Filter for Autonomous Driving with VQ-VAE and Differentiable Optimization
Sampling trajectories from a distribution followed by ranking them based on a specified cost function is a common approach in autonomous driving. Typically, the sampling distribution is hand-crafted (e.g a Gaussian, or a grid). Recently, there have been efforts towards learning the sampling distribution through generative models such as Conditional Variational Autoencoder (CVAE). However, these approaches fail to capture the multi-modality of the driving behaviour due to the Gaussian latent pri…

This https://arxiv.org/abs/2403.19461 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Sampling Distribution and Safety Filter for Autonomous Driving with VQ-VAE and Differentiable Optimization
Sampling trajectories from a distribution followed by ranking them based on a specified cost function is a common approach in autonomous driving. Typically, the sampling distribution is hand-crafted (e.g a Gaussian, or a grid). Recently, there have been efforts towards learning the sampling distribution through generative models such as Conditional Variational Autoencoder (CVAE). However, these approaches fail to capture the multi-modality of the driving behaviour due to the Gaussian latent pri…

A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains
Shuo Ling, Liwei Tan, Wenjun Ying
https://arxiv.org/abs/2404.15242

A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains
The Kernel-Free Boundary Integral (KFBI) method presents an iterative solution to boundary integral equations arising from elliptic partial differential equations (PDEs). This method effectively addresses elliptic PDEs on irregular domains, including the modified Helmholtz, Stokes, and elasticity equations. The rapid evolution of neural networks and deep learning has invigorated the exploration of numerical PDEs. An increasing interest is observed in deep learning approaches that seamlessly int…

This https://arxiv.org/abs/2403.07592 has been replaced.
link: https://scholar.google.com/scholar?q=a

Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features
Recent advancements in Spatial Transcriptomics (ST) technology have facilitated detailed gene expression analysis within tissue contexts. However, the high costs and methodological limitations of ST necessitate a more robust predictive model. In response, this paper introduces TRIPLEX, a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features, capturing cellular morphology at individual spots,…

This https://arxiv.org/abs/2404.14367 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
Learning from preference labels plays a crucial role in fine-tuning large language models. There are several distinct approaches for preference fine-tuning, including supervised learning, on-policy reinforcement learning (RL), and contrastive learning. Different methods come with different implementation tradeoffs and performance differences, and existing empirical findings present different conclusions, for instance, some results show that online RL is quite important to attain good fine-tunin…

Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
Raphael Koster, Miruna P\^islar, Andrea Tacchetti, Jan Balaguer, Leqi Liu, Romuald Elie, Oliver P. Hauser, Karl Tuyls, Matt Botvinick, Christopher Summerfield
https://arxiv.org/abs/2404.15059<…

Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves. What resource allocation mechanisms will encourage levels of reciprocation that sustain the commons? Here, in an iterated multiplayer trust game, we use deep reinforcement learning (RL) to design an allocation mechanism that endogenously promotes sustainable contributions from human participants to a common pool reso…

This https://arxiv.org/abs/2404.11003 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification
Semi-supervised image classification, leveraging pseudo supervision and consistency regularization, has demonstrated remarkable success. However, the ongoing challenge lies in fully exploiting the potential of unlabeled data. To address this, we employ information entropy neural estimation to harness the potential of unlabeled samples. Inspired by contrastive learning, the entropy is estimated by maximizing a lower bound on mutual information across different augmented views. Moreover, we theor…

MultiSTOP: Solving Functional Equations with Reinforcement Learning
Alessandro Trenta, Davide Bacciu, Andrea Cossu, Pietro Ferrero
https://arxiv.org/abs/2404.14909

MultiSTOP: Solving Functional Equations with Reinforcement Learning
We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on them. We extend the original BootSTOP algorithm by adding multiple constraints derived from domain-specific knowledge, even in integral form, to improve the accuracy of the solution. We investigate a particular equation in a one-dimensional Conformal Field Theory.

Revisiting Neural Networks for Continual Learning: An Architectural Perspective
Aojun Lu, Tao Feng, Hangjie Yuan, Xiaotian Song, Yanan Sun
https://arxiv.org/abs/2404.14829

Revisiting Neural Networks for Continual Learning: An Architectural Perspective
Efforts to overcome catastrophic forgetting have primarily centered around developing more effective Continual Learning (CL) methods. In contrast, less attention was devoted to analyzing the role of network architecture design (e.g., network depth, width, and components) in contributing to CL. This paper seeks to bridge this gap between network architecture design and CL, and to present a holistic study on the impact of network architectures on CL. This work considers architecture design at the…

Transformer-XL for Long Sequence Tasks in Robotic Learning from Demonstration
Gao Tianci
https://arxiv.org/abs/2405.15562 https://arx…

This https://arxiv.org/abs/2310.05348 has been replaced.
link: https://scholar.google.com/scholar?q=a

Continuous Invariance Learning
Invariance learning methods aim to learn invariant features in the hope that they generalize under distributional shifts. Although many tasks are naturally characterized by continuous domains, current invariance learning techniques generally assume categorically indexed domains. For example, auto-scaling in cloud computing often needs a CPU utilization prediction model that generalizes across different times (e.g., time of a day and date of a year), where `time' is a continuous domain index. In…

A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation
Phoebe Jing, Yijing Gao, Xianlong Zeng
https://arxiv.org/abs/2404.14746

A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation
In the field of fraud detection, the availability of comprehensive and privacy-compliant datasets is crucial for advancing machine learning research and developing effective anti-fraud systems. Traditional datasets often focus on transaction-level information, which, while useful, overlooks the broader context of customer behavior patterns that are essential for detecting sophisticated fraud schemes. The scarcity of such data, primarily due to privacy concerns, significantly hampers the develop…

Logic Dynamic Movement Primitives for Long-horizon Manipulation Tasks in Dynamic Environments
Yan Zhang, Teng Xue, Amirreza Razmjoo, Sylvain Calinon
https://arxiv.org/abs/2404.16138

Logic Dynamic Movement Primitives for Long-horizon Manipulation Tasks in Dynamic Environments
Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitating, generalizing, and reacting to disturbances for long-horizon manipulation tasks in dynamic environments remains a challenge. To tackle this challenge, we present Logic Dynamic Movement Primitives (Logic-DMP), which combines Task and Motion Planning (TAMP) with an optimal control formulation of DMP, allowing us to i…

Symbolic Integration Algorithm Selection with Machine Learning: LSTMs vs Tree LSTMs
Rashid Barket, Matthew England, J\"urgen Gerhard
https://arxiv.org/abs/2404.14973

Symbolic Integration Algorithm Selection with Machine Learning: LSTMs vs Tree LSTMs
Computer Algebra Systems (e.g. Maple) are used in research, education, and industrial settings. One of their key functionalities is symbolic integration, where there are many sub-algorithms to choose from that can affect the form of the output integral, and the runtime. Choosing the right sub-algorithm for a given problem is challenging: we hypothesise that Machine Learning can guide this sub-algorithm choice. A key consideration of our methodology is how to represent the mathematics to the ML …

Logic Dynamic Movement Primitives for Long-horizon Manipulation Tasks in Dynamic Environments
Yan Zhang, Teng Xue, Amirreza Razmjoo, Sylvain Calinon
https://arxiv.org/abs/2404.16138

Logic Dynamic Movement Primitives for Long-horizon Manipulation Tasks in Dynamic Environments
Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitating, generalizing, and reacting to disturbances for long-horizon manipulation tasks in dynamic environments remains a challenge. To tackle this challenge, we present Logic Dynamic Movement Primitives (Logic-DMP), which combines Task and Motion Planning (TAMP) with an optimal control formulation of DMP, allowing us to i…

This https://arxiv.org/abs/2404.07518 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Remembering Transformer for Continual Learning
Neural networks encounter the challenge of Catastrophic Forgetting (CF) in continual learning, where new task knowledge interferes with previously learned knowledge. We propose Remembering Transformer, inspired by the brain's Complementary Learning Systems (CLS), to tackle this issue. Remembering Transformer employs a mixture-of-adapters and a generative model-based routing mechanism to alleviate CF by dynamically routing task data to relevant adapters. Our approach demonstrated a new SOTA perf…

$\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning
Kerstin Kl\"aser, B{\l}a\.zej Banaszewski, Samuel Maddrell-Mander, Callum McLean, Luis M\"uller, Ali Parviz, Shenyang Huang, Andrew Fitzgibbon
https://arxiv.org/abs/2404.14986

$\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning
In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data and then transfer to low-data downstream tasks is a promising direction. However, how to design effective foundation models for molecular learning remains an open question, with existing approaches typically focusing on models with large parameter capacities. In this work, we propose $\texttt{MiniMol}$, a foundational …

This https://arxiv.org/abs/2305.15557 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Non-Parametric Learning of Stochastic Differential Equations with Non-asymptotic Fast Rates of Convergence
We propose a novel non-parametric learning paradigm for the identification of drift and diffusion coefficients of multi-dimensional non-linear stochastic differential equations, which relies upon discrete-time observations of the state. The key idea essentially consists of fitting a RKHS-based approximation of the corresponding Fokker-Planck equation to such observations, yielding theoretical estimates of non-asymptotic learning rates which, unlike previous works, become increasingly tighter wh…

This https://arxiv.org/abs/2404.13327 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Comparative Analysis on Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques
The rapid advancement of machine learning techniques has led to their widespread application in various domains including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a state-of-the-art (SOTA) deep learning sequential model, leveraging the Temporal Convolutional Network (TCN), for snowmelt-driven discharge modeling in the Himalayan basin of the Hindu Kush Himalayan Region. To evaluate the performance of our propose…