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05/23/2023
DUBLIN – Document Understanding By Language-Image Network
Visual document understanding is a complex task that involves analyzing ...
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05/11/2023
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
Deep generative models have recently achieved superior performance in 3D...
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04/22/2023
LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
Large language models (LLMs) have demonstrated remarkable zero-shot gene...
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03/23/2023
Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation
In recommendation systems, a large portion of the ratings are missing du...
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02/07/2023
RoNet: Toward Robust Neural Assisted Mobile Network Configuration
Automating configuration is the key path to achieving zero-touch network...
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12/20/2022
Goal-oriented Autonomous Driving
Modern autonomous driving system is characterized as modular tasks in se...
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10/06/2022
Neural Volumetric Mesh Generator
Deep generative models have shown success in generating 3D shapes with d...
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09/29/2022
Rectified Flow: A Marginal Preserving Approach to Optimal Transport
We present a flow-based approach to the optimal transport (OT) problem b...
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09/07/2022
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
We present rectified flow, a surprisingly simple approach to learning (n...
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09/02/2022
First Hitting Diffusion Models
We propose a family of First Hitting Diffusion Models (FHDM), deep gener...
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08/31/2022
Let us Build Bridges: Understanding and Extending Diffusion Generative Models
Diffusion-based generative models have achieved promising results recent...
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08/22/2022
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
A big convergence of language, vision, and multimodal pretraining is eme...
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07/29/2022
Deep Reinforcement Learning for End-to-End Network Slicing: Challenges and Solutions
5G and beyond is expected to enable various emerging use cases with dive...
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06/27/2022
Split Localized Conformal Prediction
Conformal prediction is a simple and powerful tool that can quantify unc...
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03/14/2022
WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition
Named Entity Recognition task is one of the core tasks of information ex...
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12/02/2021
FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization
Generating images from natural language instructions is an intriguing ye...
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04/26/2021
Improve Vision Transformers Training by Suppressing Over-smoothing
Introducing the transformer structure into computer vision tasks holds t...
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03/04/2021
Deep Graph Structure Learning for Robust Representations: A Survey
Graph Neural Networks (GNNs) are widely used for analyzing graph-structu...
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01/11/2021
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
Click-through rate (CTR) prediction, which aims to predict the probabili...
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03/23/2020
Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting
We propose signed splitting steepest descent (S3D), which progressively ...
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02/20/2020
MaxUp: A Simple Way to Improve Generalization of Neural Network Training
We propose MaxUp, an embarrassingly simple, highly effective technique f...
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10/12/2018
On the Margin Theory of Feedforward Neural Networks
Past works have shown that, somewhat surprisingly, over-parametrization ...
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11/20/2017
Structured Stein Variational Inference for Continuous Graphical Models
We propose a novel distributed inference algorithm for continuous graphi...
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11/07/2017
On the Discrimination-Generalization Tradeoff in GANs
Generative adversarial training can be generally understood as minimizin...
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07/04/2017
Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE
We propose a number of new algorithms for learning deep energy models an...
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08/16/2016
