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Found 22 papers, 1 papers with code
Transformers learn to implement preconditioned gradient descent for in-context learning
Motivated by the striking ability of transformers for in-context learning, several works demonstrate that transformers can implement algorithms like gradient descent.
Multi-timescale Trading Strategy for Renewable Power to Ammonia Virtual Power Plant in the Electricity, Hydrogen, and Ammonia Markets
Hence, we develop a multi-timescale trading strategy for the RePtA VPP in the electricity, hydrogen, and ammonia markets.
Unsafe's Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering via Machine Learning
Memory-safety bugs introduce critical software-security issues.
Toward 6G with Terahertz Communications: Understanding the Propagation Channels
This article aims at providing insights for a comprehensive understanding of terahertz (THz) propagation channels.
Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning
Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding.
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm
Conventional wisdom in the sampling literature, backed by a popular diffusion scaling limit, suggests that the mixing time of the Metropolis-Adjusted Langevin Algorithm (MALA) scales as $O(d^{1/3})$, where $d$ is the dimension.
KHOVID: Interoperable Privacy Preserving Digital Contact Tracing
To accelerate the laborious manual contact tracing process, digital contact tracing (DCT) tools can track contact events transparently and privately by using the sensing and signaling capabilities of the ubiquitous cell phone.
Cryptography and Security Computers and Society
An End-to-End Solution for Named Entity Recognition in eCommerce Search
Beyond our application, this TripleLearn framework, as well as the end-to-end process, is model-independent and problem-independent, so it can be generalized to more industrial applications, especially to the eCommerce industry which has similar data foundations and problems.
Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy Inference System
Deep neural networks (DNNs) demonstrate great success in classification tasks.
Tuning Convolutional Spiking Neural Network with Biologically-plausible Reward Propagation
The performance of the proposed BRP-SNN is further verified on the spatial (including MNIST and Cifar-10) and temporal (including TIDigits and DvsGesture) tasks, where the SNN using BRP has reached a similar accuracy compared to other state-of-the-art BP-based SNNs and saved 50% more computational cost than ANNs.
Finite Meta-Dynamic Neurons in Spiking Neural Networks for Spatio-temporal Learning
Spiking Neural Networks (SNNs) have incorporated more biologically-plausible structures and learning principles, hence are playing critical roles in bridging the gap between artificial and natural neural networks.
Cooperative Reasoning on Knowledge Graph and Corpus: A Multi-agentReinforcement Learning Approach
We model the question answering on KG as a cooperative task between two agents, a knowledge graph reasoning agent and an information extraction agent.
Learning to Order Sub-questions for Complex Question Answering
Answering complex questions involving multiple entities and relations is a challenging task.
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning
Knowledge graphs (KGs) often suffer from sparseness and incompleteness.
Stochastic Gradient and Langevin Processes
We prove quantitative convergence rates at which discrete Langevin-like processes converge to the invariant distribution of a related stochastic differential equation.
Is There an Analog of Nesterov Acceleration for MCMC?
We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective functional.
Quantitative Weak Convergence for Discrete Stochastic Processes
In this paper, we quantitative convergence in $W_2$ for a family of Langevin-like stochastic processes that includes stochastic gradient descent and related gradient-based algorithms.
Sharp convergence rates for Langevin dynamics in the nonconvex setting
We study the problem of sampling from a distribution $p^*(x) \propto \exp\left(-U(x)\right)$, where the function $U$ is $L$-smooth everywhere and $m$-strongly convex outside a ball of radius $R$, but potentially nonconvex inside this ball.
Underdamped Langevin MCMC: A non-asymptotic analysis
We study the underdamped Langevin diffusion when the log of the target distribution is smooth and strongly concave.
Convergence of Langevin MCMC in KL-divergence
Langevin diffusion is a commonly used tool for sampling from a given distribution.
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
We consider first order gradient methods for effectively optimizing a composite objective in the form of a sum of smooth and, potentially, non-smooth functions.
Asymptotic behavior of $\ell_p$-based Laplacian regularization in semi-supervised learning
Together, these properties show that $p = d+1$ is an optimal choice, yielding a function estimate $\hat{f}$ that is both smooth and non-degenerate, while remaining maximally sensitive to $P$.
