research
∙
06/26/2023
Restart Sampling for Improving Generative Processes
Generative processes that involve solving differential equations, such a...
share
research
∙
06/21/2023
Block-Wise Index Modulation and Receiver Design for High-Mobility OTFS Communications
As a promising technique for high-mobility wireless communications, orth...
research
∙
06/01/2023
Transformers learn to implement preconditioned gradient descent for in-context learning
Motivated by the striking ability of transformers for in-context learnin...
research
∙
10/31/2022
Unsafe's Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering via Machine Learning
Memory-safety bugs introduce critical software-security issues. Rust pro...
research
∙
06/15/2021
Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning
With the Deep Neural Networks (DNNs) as a powerful function approximator...
research
∙
03/07/2021
Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples
Fuzzy similarity join is an important database operator widely used in p...
research
∙
12/23/2020
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm
Conventional wisdom in the sampling literature, backed by a popular diff...
research
∙
12/17/2020
KHOVID: Interoperable Privacy Preserving Digital Contact Tracing
During a pandemic, contact tracing is an essential tool to drive down th...
research
∙
12/11/2020
An End-to-End Solution for Named Entity Recognition in eCommerce Search
Named entity recognition (NER) is a critical step in modern search query...
research
∙
10/10/2020
Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy Inference System
Deep neural networks (DNNs) demonstrate great success in classification ...
research
∙
10/09/2020
Tuning Convolutional Spiking Neural Network with Biologically-plausible Reward Propagation
Spiking Neural Networks (SNNs) contain more biology-realistic structures...
research
∙
10/07/2020
Finite Meta-Dynamic Neurons in Spiking Neural Networks for Spatio-temporal Learning
Spiking Neural Networks (SNNs) have incorporated more biologically-plaus...
research
∙
09/14/2020
Answering Multi-Dimensional Range Queries under Local Differential Privacy
In this paper, we tackle the problem of answering multi-dimensional rang...
research
∙
12/04/2019
Cooperative Reasoning on Knowledge Graph and Corpus: A Multi-agentReinforcement Learning Approach
Knowledge-graph-based reasoning has drawn a lot of attention due to its ...
research
∙
11/11/2019
Learning to Order Sub-questions for Complex Question Answering
Answering complex questions involving multiple entities and relations is...
research
∙
07/07/2019
Quantitative W_1 Convergence of Langevin-Like Stochastic Processes with Non-Convex Potential State-Dependent Noise
We prove quantitative convergence rates at which discrete Langevin-like ...
research
∙
02/04/2019
Is There an Analog of Nesterov Acceleration for MCMC?
We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as ...
research
∙
02/03/2019
Quantitative Central Limit Theorems for Discrete Stochastic Processes
In this paper, we establish a generalization of the classical Central Li...
research
∙
05/04/2018
Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting
We study the problem of sampling from a distribution where the negative ...
research
∙
12/18/2017
Index Modulation for 5G: Striving to Do More with Less
The fifth generation (5G) wireless communications brag both high spectru...
research
∙
07/12/2017
Underdamped Langevin MCMC: A non-asymptotic analysis
We study the underdamped Langevin diffusion when the log of the target d...
research
∙
05/25/2017
Convergence of Langevin MCMC in KL-divergence
Langevin diffusion is a commonly used tool for sampling from a given dis...
research
∙
05/26/2016
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
We consider first order gradient methods for effectively optimizing a co...
research
∙
03/02/2016
