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09/02/2023
GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning
This paper investigates a challenging problem of zero-shot learning in t...
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08/30/2023
Hanayo: Harnessing Wave-like Pipeline Parallelism for Enhanced Large Model Training Efficiency
Large-scale language models have become increasingly challenging and exp...
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06/30/2023
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
Do neural networks, trained on well-understood algorithmic tasks, reliab...
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06/26/2023
Restart Sampling for Improving Generative Processes
Generative processes that involve solving differential equations, such a...
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05/31/2023
Discovering New Interpretable Conservation Laws as Sparse Invariants
Discovering conservation laws for a given dynamical system is important ...
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05/04/2023
Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability
We introduce Brain-Inspired Modular Training (BIMT), a method for making...
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05/02/2023
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning
Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts...
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04/05/2023
GenPhys: From Physical Processes to Generative Models
Since diffusion models (DM) and the more recent Poisson flow generative ...
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03/23/2023
The Quantization Model of Neural Scaling
We propose the Quantization Model of neural scaling laws, explaining bot...
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02/08/2023
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models
We introduce a new family of physics-inspired generative models termed P...
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01/20/2023
ATP: Adaptive Tensor Parallelism for Foundation Models
Foundation models have impressive performance and generalization capabil...
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11/19/2022
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel concepts...
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10/24/2022
Precision Machine Learning
We explore unique considerations involved in fitting ML models to data w...
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10/03/2022
Omnigrok: Grokking Beyond Algorithmic Data
Grokking, the unusual phenomenon for algorithmic datasets where generali...
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09/22/2022
Poisson Flow Generative Models
We propose a new "Poisson flow" generative model (PFGM) that maps a unif...
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09/06/2022
EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models
Large transformer models display promising performance on a wide range o...
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08/21/2022
Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network
Federated learning (FL) has emerged as a promising privacy-preserving di...
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08/09/2022
Second Order Ensemble Langevin Method for Sampling and Inverse Problems
We propose a sampling method based on an ensemble approximation of secon...
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06/13/2022
Accelerating Federated Learning via Sampling Anchor Clients with Large Batches
Using large batches in recent federated learning studies has improved co...
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05/20/2022
Towards Understanding Grokking: An Effective Theory of Representation Learning
We aim to understand grokking, a phenomenon where models generalize long...
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03/23/2022
AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations
We present a machine learning algorithm that discovers conservation laws...
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03/07/2022
Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention
Multi-label zero-shot learning extends conventional single-label zero-sh...
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12/17/2021
From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization
In the setting of federated optimization, where a global model is aggreg...
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09/28/2021
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
Integrating physical inductive biases into machine learning can improve ...
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09/20/2021
Machine-learning hidden symmetries
We present an automated method for finding hidden symmetries, defined as...
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05/31/2021
Machine-Learning Non-Conservative Dynamics for New-Physics Detection
Energy conservation is a basic physics principle, the breakdown of which...
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11/09/2020
AI Poincaré: Machine Learning Conservation Laws from Trajectories
We present AI Poincaré, a machine learning algorithm for auto-discoverin...
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06/13/2020
DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition
State-of-the-art video action recognition models with complex network ar...
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06/13/2020
HRDNet: High-resolution Detection Network for Small Objects
Small object detection is challenging because small objects do not conta...
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06/08/2020
Schrödinger PCA: You Only Need Variances for Eigenmodes
Principal component analysis (PCA) has achieved great success in unsuper...
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12/06/2019
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution
In this article, we focus on the analysis of the potential factors drivi...
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12/04/2019
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling
Hamiltonian Monte Carlo (HMC) is an efficient Bayesian sampling method t...
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12/02/2019