Search Results for author:
Found 16 papers, 7 papers with code
FloE: On-the-Fly MoE Inference
With the widespread adoption of Mixture-of-Experts (MoE) models, there is a growing demand for efficient inference on memory-constrained devices.
Reinforcement Learning for Reasoning in Large Language Models with One Training Example
We also show the critical role of promoting exploration (e. g., by adding entropy loss with an appropriate coefficient) in 1-shot RLVR training.
DrivAer Transformer: A high-precision and fast prediction method for vehicle aerodynamic drag coefficient based on the DrivAerNet++ dataset
At the current stage, deep learning-based methods have demonstrated excellent capabilities in evaluating aerodynamic performance, significantly reducing the time and cost required for traditional computational fluid dynamics (CFD) simulations.
Is Your World Simulator a Good Story Presenter? A Consecutive Events-Based Benchmark for Future Long Video Generation
However, they still struggle to coherently present multiple sequential events in the stories specified by the prompts, which is foreseeable an essential capability for future long video generation scenarios.
Mojito: Motion Trajectory and Intensity Control for Video Generation
Recent advancements in diffusion models have shown great promise in producing high-quality video content.
Infer Human's Intentions Before Following Natural Language Instructions
We propose a new framework, Follow Instructions with Social and Embodied Reasoning (FISER), aiming for better natural language instruction following in collaborative embodied tasks.
DPSW-Sketch: A Differentially Private Sketch Framework for Frequency Estimation over Sliding Windows (Technical Report)
The sliding window model of computation captures scenarios in which data are continually arriving in the form of a stream, and only the most recent $w$ items are used for analysis.
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning
Three main data selection approaches are: (1) leveraging external non-CLIP models to aid data selection, (2) training new CLIP-style embedding models that are more effective at selecting high-quality data than the original OpenAI CLIP model, and (3) designing better metrics or strategies universally applicable to any CLIP embedding without requiring specific model properties (e. g., CLIPScore is one popular metric).
Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive Learning
In recent years, data selection has emerged as a core issue for large-scale visual-language model pretraining, especially on noisy web-curated datasets.
JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention
We propose Joint MLP/Attention (JoMA) dynamics, a novel mathematical framework to understand the training procedure of multilayer Transformer architectures.
Improved Active Multi-Task Representation Learning via Lasso
In addition to our sample complexity results, we also characterize the potential of our $\nu^1$-based strategy in sample-cost-sensitive settings.
C-Mixup: Improving Generalization in Regression
In this paper, we propose a simple yet powerful algorithm, C-Mixup, to improve generalization on regression tasks.
CT-SGAN: Computed Tomography Synthesis GAN
Diversity in data is critical for the successful training of deep learning models.
MASAI: Multi-agent Summative Assessment Improvement for Unsupervised Environment Design
We qualitatively and quantitatively demonstrate that, in terms of multi-agent ($\geq$ 8 agents) navigation and steering, $\textit{Students}$ trained by our approach outperform agents using heuristic search, as well as agents trained by domain randomization.
Conditional Generation of Medical Images via Disentangled Adversarial Inference
Current practices in using cGANs for medical image generation, only use a single variable for image generation (i. e., content) and therefore, do not provide much flexibility nor control over the generated image.
Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning
The proposed algorithm achieved a mean accuracy of $87. 54\%$ and Cohen's kappa of $0. 8106$ in the slide-level classification of $305$ WSIs; performing better than a standard CNN and pathologists without gynecology-specific training.
