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06/07/2023
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection
Neural sequence models based on the transformer architecture have demons...
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03/23/2023
ENVIDR: Implicit Differentiable Renderer with Neural Environment Lighting
Recent advances in neural rendering have shown great potential for recon...
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03/19/2023
Provable Convergence of Variational Monte Carlo Methods
The Variational Monte Carlo (VMC) is a promising approach for computing ...
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02/21/2023
Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge
Semantic consistency recognition aims to detect and judge whether the se...
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02/16/2023
QTrojan: A Circuit Backdoor Against Quantum Neural Networks
We propose a circuit-level backdoor attack, QTrojan, against Quantum Neu...
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02/02/2023
Lower Bounds for Learning in Revealing POMDPs
This paper studies the fundamental limits of reinforcement learning (RL)...
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12/06/2022
Uniform-in-Time Propagation of Chaos for Mean Field Langevin Dynamics
We study the uniform-in-time propagation of chaos for mean field Langevi...
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11/24/2022
CryptoLight: An Electro-Optical Accelerator for Fully Homomorphic Encryption
Fully homomorphic encryption (FHE) protects data privacy in cloud comput...
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09/29/2022
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms
Partial Observability – where agents can only observe partial informatio...
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09/23/2022
Unified Algorithms for RL with Decision-Estimation Coefficients: No-Regret, PAC, and Reward-Free Learning
Finding unified complexity measures and algorithms for sample-efficient ...
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07/13/2022
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP
As an important framework for safe Reinforcement Learning, the Constrain...
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04/12/2022
Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization
A major challenge in multi-agent systems is that the system complexity g...
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11/08/2021
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy
Aiming at recognizing the samples from novel categories with few referen...
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10/26/2021
Plug-and-Play Few-shot Object Detection with Meta Strategy and Explicit Localization Inference
Aiming at recognizing and localizing the object of novel categories by a...
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08/06/2021
Estimating Graph Dimension with Cross-validated Eigenvalues
In applied multivariate statistics, estimating the number of latent dime...
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12/15/2020
Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning
This paper proposes Friedrichs learning as a novel deep learning methodo...
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11/06/2020
Low-Cost Floating-Point Processing in ReRAM for Scientific Computing
We propose ReFloat, a principled approach for low-cost floating-point pr...
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07/01/2020
A New Basis for Sparse PCA
The statistical and computational performance of sparse principal compon...
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04/30/2020
White Paper on 6G Drivers and the UN SDGs
The commercial launch of 6G communications systems and United Nations Su...
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10/04/2019
Targeted sampling from massive Blockmodel graphs with personalized PageRank
This paper provides statistical theory and intuition for Personalized Pa...
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09/18/2019
Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment
Emerging resistive random-access memory (ReRAM) has recently been intens...
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02/02/2019