Search Results for author:
Found 25 papers, 9 papers with code
Efficient Neural Theorem Proving via Fine-grained Proof Structure Analysis
The superiority of our method is validated on the miniF2F-test benchmark using the open-source deepseek-math-7b-base model and the Isabelle proof assistant.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
GTA: Global Tracklet Association for Multi-Object Tracking in Sports
The proposed method achieved a new state-of-the-art performance on the SportsMOT dataset with HOTA score of 81. 04%.
Ranked #1 on
Multiple Object Tracking
on SportsMOT
(using extra training data)
How Numerical Precision Affects Mathematical Reasoning Capabilities of LLMs
Despite the remarkable success of Transformer-based Large Language Models (LLMs) across various domains, understanding and enhancing their mathematical capabilities remains a significant challenge.
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data
In this paper, we reveal that the concrete score in absorbing diffusion can be expressed as conditional probabilities of clean data, multiplied by a time-dependent scalar in an analytic form.
The Buffer Mechanism for Multi-Step Information Reasoning in Language Models
Large language models have consistently struggled with complex reasoning tasks, such as mathematical problem-solving.
Elucidating The Design Space of Classifier-Guided Diffusion Generation
Guidance in conditional diffusion generation is of great importance for sample quality and controllability.
Ranked #1 on
Conditional Image Generation
on ImageNet 128x128
SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
Based on our analysis, we propose SA-Solver, which is an improved efficient stochastic Adams method for solving diffusion SDE to generate data with high quality.
Ranked #32 on
Image Generation
on ImageNet 512x512
Training Energy-Based Models with Diffusion Contrastive Divergences
In image generation experiments, the proposed DCD is capable of training an energy-based model for generating the Celab-A $32\times 32$ dataset, which is comparable to existing EBMs.
Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports
Additionally, relying on the Kalman filter in recent tracking algorithms falls short when object motion defies its linear assumption.
Ranked #5 on
Multiple Object Tracking
on SportsMOT
(using extra training data)
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
To demonstrate the effectiveness and universality of Diff-Instruct, we consider two scenarios: distilling pre-trained diffusion models and refining existing GAN models.
On the Generalization of Diffusion Model
To understand this contradiction, we empirically verify the difference between the sufficiently trained diffusion model and the empirical optima.
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models.
Why Are Conditional Generative Models Better Than Unconditional Ones?
Extensive empirical evidence demonstrates that conditional generative models are easier to train and perform better than unconditional ones by exploiting the labels of data.
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
Thus, the generation performance on a subset of timesteps is crucial, which is greatly influenced by the covariance design in DPMs.
Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks
Gradient-based methods for the distributed training of residual networks (ResNets) typically require a forward pass of the input data, followed by back-propagating the error gradient to update model parameters, which becomes time-consuming as the network goes deeper.
Nonlinear ICA Using Volume-Preserving Transformations
Nonlinear ICA is a fundamental problem in machine learning, aiming to identify the underlying independent components (sources) from data which is assumed to be a nonlinear function (mixing function) of these sources.
Rethinking Adversarial Transferability from a Data Distribution Perspective
Adversarial transferability enables attackers to generate adversarial examples from the source model to attack the target model, which has raised security concerns about the deployment of DNNs in practice.
Layer-Parallel Training of Residual Networks with Auxiliary Variables
Backpropagation algorithm is indispensable for training modern residual networks (ResNets) and usually tends to be time-consuming due to its inherent algorithmic lockings.
Towards Understanding the Generative Capability of Adversarially Robust Classifiers
We find that adversarial training contributes to obtaining an energy function that is flat and has low energy around the real data, which is the key for generative capability.
Improved OOD Generalization via Adversarial Training and Pre-training
In this paper, after defining OOD generalization via Wasserstein distance, we theoretically show that a model robust to input perturbation generalizes well on OOD data.
SAD: Saliency Adversarial Defense without Adversarial Training
We justify the algorithm with a linear model that the added saliency maps pull data away from its closest decision boundary.
Batch Group Normalization
Among previous normalization methods, Batch Normalization (BN) performs well at medium and large batch sizes and is with good generalizability to multiple vision tasks, while its performance degrades significantly at small batch sizes.
A Practical Layer-Parallel Training Algorithm for Residual Networks
Gradient-based algorithms for training ResNets typically require a forward pass of the input data, followed by back-propagating the objective gradient to update parameters, which are time-consuming for deep ResNets.
New Interpretations of Normalization Methods in Deep Learning
In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc.
DARTS+: Improved Differentiable Architecture Search with Early Stopping
Therefore, we propose a simple and effective algorithm, named "DARTS+", to avoid the collapse and improve the original DARTS, by "early stopping" the search procedure when meeting a certain criterion.
