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09/02/2023
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model
Text-to-image generation (TTI) refers to the usage of models that could ...
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08/22/2023
ShadowNet for Data-Centric Quantum System Learning
Understanding the dynamics of large quantum systems is hindered by the c...
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08/10/2023
Deformable Mixer Transformer with Gating for Multi-Task Learning of Dense Prediction
CNNs and Transformers have their own advantages and both have been widel...
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08/03/2023
Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants
How to enable learnability for new classes while keeping the capability ...
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04/13/2023
Asymmetrically-powered Neural Image Compression with Shallow Decoders
Neural image compression methods have seen increasingly strong performan...
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02/06/2023
Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning
Few-shot class-incremental learning (FSCIL) has been a challenging probl...
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01/09/2023
DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction
Convolution neural networks (CNNs) and Transformers have their own advan...
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01/03/2023
Understanding Imbalanced Semantic Segmentation Through Neural Collapse
A recent study has shown a phenomenon called neural collapse in that the...
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01/03/2023
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation
Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part ...
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10/12/2022
Towards Theoretically Inspired Neural Initialization Optimization
Automated machine learning has been widely explored to reduce human effo...
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07/13/2022
Eliminating Gradient Conflict in Reference-based Line-art Colorization
Reference-based line-art colorization is a challenging task in computer ...
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07/10/2022
SFNet: Faster, Accurate, and Domain Agnostic Semantic Segmentation via Semantic Flow
In this paper, we focus on exploring effective methods for faster, accur...
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06/19/2022
EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm
Motivated by biological evolution, this paper explains the rationality o...
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04/19/2022
Neural Collapse Inspired Attraction-Repulsion-Balanced Loss for Imbalanced Learning
Class imbalance distribution widely exists in real-world engineering. Ho...
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03/17/2022
Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Modern deep neural networks for classification usually jointly learn a b...
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03/08/2022
ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation
Point cloud classifiers with rotation robustness have been widely discus...
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03/06/2022
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors
We present a simple and effective approach for posterior uncertainty qua...
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02/14/2022
An Introduction to Neural Data Compression
Neural compression is the application of neural networks and other machi...
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01/13/2022
TransVOD: End-to-end Video Object Detection with Spatial-Temporal Transformers
Detection Transformer (DETR) and Deformable DETR have been proposed to e...
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12/04/2021
Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint
Deformable image registration is able to achieve fast and accurate align...
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11/23/2021
Towards Empirical Sandwich Bounds on the Rate-Distortion Function
Rate-distortion (R-D) function, a key quantity in information theory, ch...
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09/30/2021
Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks
We develop biologically plausible training mechanisms for self-supervise...
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07/28/2021
Insights from Generative Modeling for Neural Video Compression
While recent machine learning research has revealed connections between ...
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02/19/2021
Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs
We present a new type of acquisition functions for online decision makin...
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01/27/2021
Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search
Most differentiable neural architecture search methods construct a super...
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11/18/2020
Explicitly Learning Topology for Differentiable Neural Architecture Search
Differentiable neural architecture search (DARTS) has gained much succes...
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10/19/2020
Hierarchical Autoregressive Modeling for Neural Video Compression
Recent work by Marino et al. (2020) showed improved performance in seque...
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10/13/2020
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding
Neural architecture search (NAS) aims to produce the optimal sparse solu...
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06/07/2020
Improving Inference for Neural Image Compression
We consider the problem of lossy image compression with deep latent vari...
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04/15/2020
Bayesian differential programming for robust systems identification under uncertainty
This paper presents a machine learning framework for Bayesian systems id...
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03/23/2020
Spatial Pyramid Based Graph Reasoning for Semantic Segmentation
The convolution operation suffers from a limited receptive filed, while ...
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02/25/2020
Exact artificial boundary conditions of 1D semi-discretized peridynamics
The peridynamic theory reformulates the equations of continuum mechanics...
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02/18/2020
Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding
Deep Bayesian latent variable models have enabled new approaches to both...
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01/08/2020
Lifted Hybrid Variational Inference
A variety of lifted inference algorithms, which exploit model symmetry t...
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11/23/2019
Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families
The correspondence between residual networks and dynamical systems motiv...
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11/18/2019
SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
The panoptic segmentation task requires a unified result from semantic a...
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07/31/2019
Expectation-Maximization Attention Networks for Semantic Segmentation
Self-attention mechanism has been widely used for various tasks. It is d...
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05/13/2019
Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning
Advances in computational science offer a principled pipeline for predic...
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01/15/2019
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
We present a probabilistic deep learning methodology that enables the co...
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12/09/2018
Physics-informed deep generative models
We consider the application of deep generative models in propagating unc...
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11/09/2018
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
We present a deep learning framework for quantifying and propagating unc...
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10/03/2018
Optimization Algorithm Inspired Deep Neural Network Structure Design
Deep neural networks have been one of the dominant machine learning appr...
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09/12/2018
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution
Convolutional neural networks (CNNs) have recently achieved great succes...
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06/14/2018
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization
We propose a simple and easy to implement neural network compression alg...
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02/28/2018
