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07/23/2023
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
The curse-of-dimensionality (CoD) taxes computational resources heavily ...
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06/18/2023
IF2Net: Innately Forgetting-Free Networks for Continual Learning
Continual learning can incrementally absorb new concepts without interfe...
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06/16/2023
Multi-View Class Incremental Learning
Multi-view learning (MVL) has gained great success in integrating inform...
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06/12/2023
Fast Diffusion Model
Despite their success in real data synthesis, diffusion models (DMs) oft...
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05/30/2023
How Does Information Bottleneck Help Deep Learning?
Numerous deep learning algorithms have been inspired by and understood v...
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05/28/2023
Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks
Large Language Models (LLMs) have shown promising performance in knowled...
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05/23/2023
Automatic Model Selection with Large Language Models for Reasoning
Chain-of-Thought and Program-Aided Language Models represent two distinc...
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05/01/2023
Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding
We endow Large Language Models (LLMs) with fine-grained self-evaluation ...
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04/08/2023
Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks
As machine learning has been deployed ubiquitously across applications i...
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03/01/2023
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization
In this paper, we provide an information-theoretic perspective on Varian...
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03/01/2023
D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory
Kohn-Sham Density Functional Theory (KS-DFT) has been traditionally solv...
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01/31/2023
Auxiliary Learning as an Asymmetric Bargaining Game
Auxiliary learning is an effective method for enhancing the generalizati...
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12/27/2022
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Mixup is a popular data augmentation technique for training deep neural ...
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11/16/2022
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
In this paper, we propose the augmented physics-informed neural network ...
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11/02/2022
Neural Active Learning on Heteroskedastic Distributions
Models that can actively seek out the best quality training data hold th...
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11/01/2022
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning (RL) is a promising direction fo...
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10/24/2022
GFlowOut: Dropout with Generative Flow Networks
Bayesian Inference offers principled tools to tackle many critical probl...
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10/15/2022
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Recently, implicit graph neural networks (GNNs) have been proposed to ca...
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07/22/2022
Discrete Key-Value Bottleneck
Deep neural networks perform well on prediction and classification tasks...
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06/27/2022
Robustness Implies Generalization via Data-Dependent Generalization Bounds
This paper proves that robustness implies generalization via data-depend...
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05/20/2022
Set-based Meta-Interpolation for Few-Task Meta-Learning
Meta-learning approaches enable machine learning systems to adapt to new...
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04/01/2022
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification
We introduce Simplicial Embeddings (SEMs) as a way to constrain the enco...
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02/22/2022
EIGNN: Efficient Infinite-Depth Graph Neural Networks
Graph neural networks (GNNs) are widely used for modelling graph-structu...
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02/02/2022
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
Vector Quantization (VQ) is a method for discretizing latent representat...
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02/02/2022
Multi-Task Learning as a Bargaining Game
In Multi-task learning (MTL), a joint model is trained to simultaneously...
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01/17/2022
ExpertNet: A Symbiosis of Classification and Clustering
A widely used paradigm to improve the generalization performance of high...
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01/14/2022
Training Free Graph Neural Networks for Graph Matching
We present TFGM (Training Free Graph Matching), a framework to boost the...
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12/06/2021
Noether Networks: Meta-Learning Useful Conserved Quantities
Progress in machine learning (ML) stems from a combination of data avail...
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09/20/2021
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?
Physics-informed neural networks (PINNs) have become a popular choice fo...
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07/12/2021
Meta-learning PINN loss functions
We propose a meta-learning technique for offline discovery of physics-in...
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07/06/2021
Discrete-Valued Neural Communication
Deep learning has advanced from fully connected architectures to structu...
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06/28/2021
Understanding Dynamics of Nonlinear Representation Learning and Its Application
Representations of the world environment play a crucial role in machine ...
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06/18/2021
Adversarial Training Helps Transfer Learning via Better Representations
Transfer learning aims to leverage models pre-trained on source data to ...
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06/07/2021
MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift
Given a stream of entries over time in a multi-aspect data setting where...
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05/20/2021
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions
We propose a new type of neural networks, Kronecker neural networks (KNN...
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05/10/2021
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
Graph Neural Networks (GNNs) have been studied through the lens of expre...
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04/12/2021
A Recipe for Global Convergence Guarantee in Deep Neural Networks
Existing global convergence guarantees of (stochastic) gradient descent ...
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02/23/2021
CAC: A Clustering Based Framework for Classification
In data containing heterogeneous subpopulations, classification performa...
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02/15/2021
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers
A deep equilibrium model uses implicit layers, which are implicitly defi...
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02/11/2021
When and How Mixup Improves Calibration
In many machine learning applications, it is important for the model to ...
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11/09/2020
Towards Domain-Agnostic Contrastive Learning
Despite recent success, most contrastive self-supervised learning method...
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10/09/2020
How Does Mixup Help With Robustness and Generalization?
Mixup is a popular data augmentation technique based on taking convex co...
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09/22/2020
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time
From CNNs to attention mechanisms, encoding inductive biases into neural...
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09/25/2019
Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks
We propose two approaches of locally adaptive activation functions namel...
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08/05/2019
Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes
In this paper, we theoretically prove that gradient descent can find a g...
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07/09/2019
A Stochastic First-Order Method for Ordered Empirical Risk Minimization
We propose a new stochastic first-order method for empirical risk minimi...
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04/07/2019
Every Local Minimum is a Global Minimum of an Induced Model
For non-convex optimization in machine learning, this paper proves that ...
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01/12/2019
Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit
Recent work has noted that all bad local minima can be removed from neur...
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01/02/2019
Elimination of All Bad Local Minima in Deep Learning
In this paper, we theoretically prove that we can eliminate all suboptim...
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11/20/2018