Tag: learning
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Deep Learning for Hydroelectric Optimization: Generating Long-Term River Discharge Scenarios with Ensemble Forecasts from Global Circulation Models
Deep Learning for Hydroelectric Optimization: Generating Long-Term River Discharge Scenarios with Ensemble Forecasts from Global Circulation Models arXiv:2412.12234v1 Announce Type: cross Abstract: Hydroelectric power generation is a critical component of the global energy matrix, particularly in countries like Brazil, where it represents the majority of the energy supply. However, its strong dependence on river discharges,…
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Representation learning of dynamic networks
Representation learning of dynamic networks arXiv:2412.11065v1 Announce Type: new Abstract: This study presents a novel representation learning model tailored for dynamic networks, which describes the continuously evolving relationships among individuals within a population. The problem is encapsulated in the dimension reduction topic of functional data analysis. With dynamic networks represented as matrix-valued functions, our objective…
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Deep Learning-based Approaches for State Space Models: A Selective Review
Deep Learning-based Approaches for State Space Models: A Selective Review arXiv:2412.11211v1 Announce Type: new Abstract: State-space models (SSMs) offer a powerful framework for dynamical system analysis, wherein the temporal dynamics of the system are assumed to be captured through the evolution of the latent states, which govern the values of the observations. This paper provides…
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A Statistical Analysis for Supervised Deep Learning with Exponential Families for Intrinsically Low-dimensional Data
A Statistical Analysis for Supervised Deep Learning with Exponential Families for Intrinsically Low-dimensional Data arXiv:2412.09779v1 Announce Type: new Abstract: Recent advances have revealed that the rate of convergence of the expected test error in deep supervised learning decays as a function of the intrinsic dimension and not the dimension $d$ of the input space. Existing…
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A Note on Sample Complexity of Interactive Imitation Learning with Log Loss
A Note on Sample Complexity of Interactive Imitation Learning with Log Loss arXiv:2412.07057v1 Announce Type: new Abstract: Imitation learning (IL) is a general paradigm for learning from experts in sequential decision-making problems. Recent advancements in IL have shown that offline imitation learning, specifically Behavior Cloning (BC) with log loss, is minimax optimal. Meanwhile, its interactive…
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Reinforcement Learning: Self-Driving Cars to Self-Driving Labs
Reinforcement Learning: Self-Driving Cars to Self-Driving Labs Understanding AI applications in bio for machine learning engineers Photo by Ousa Chea on Unsplash Anyone who has tried teaching a dog new tricks knows the basics of reinforcement learning. We can modify the dog’s behavior by repeatedly offering rewards for obedience and punishments for misbehavior. In reinforcement learning…
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Universal Rates of Empirical Risk Minimization
Universal Rates of Empirical Risk Minimization arXiv:2412.02810v1 Announce Type: new Abstract: The well-known empirical risk minimization (ERM) principle is the basis of many widely used machine learning algorithms, and plays an essential role in the classical PAC theory. A common description of a learning algorithm’s performance is its so-called “learning curve”, that is, the decay…
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Machine Learning Experiments Done Right
Machine Learning Experiments Done Right A detailed guideline for designing machine learning experiments that produce reliable, reproducible results. Photo by Vedrana Filipović on Unsplash Machine learning (ML) practitioners run experiments to compare the effectiveness of methods for both specific applications and for general types of problems. The validity of experimental results hinges on how practitioners design,…
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The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?
The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History? arXiv:2411.18656v1 Announce Type: new Abstract: In today’s world, AI programs powered by Machine Learning are ubiquitous, and have achieved seemingly exceptional performance across a broad range of tasks, from medical diagnosis and credit rating in banking,…
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On the ERM Principle in Meta-Learning
On the ERM Principle in Meta-Learning arXiv:2411.17898v1 Announce Type: new Abstract: Classic supervised learning involves algorithms trained on $n$ labeled examples to produce a hypothesis $h in mathcal{H}$ aimed at performing well on unseen examples. Meta-learning extends this by training across $n$ tasks, with $m$ examples per task, producing a hypothesis class $mathcal{H}$ within some…