Search Results for author:
Found 19 papers, 10 papers with code
CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision
At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations (in natural language), resulting a model able to generate better embeddings for binary code.
PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation
Open-world 3D part segmentation is pivotal in diverse applications such as robotics and AR/VR.
How Far Have We Gone in Vulnerability Detection Using Large Language Models
Given the significant successes of large language models (LLMs) in various tasks, there is growing anticipation of their efficacy in vulnerability detection.
Divergences between Language Models and Human Brains
In this work, we systematically explore the divergences between human and machine language processing by examining the differences between LM representations and human brain responses to language as measured by Magnetoencephalography (MEG) across two datasets in which subjects read and listened to narrative stories.
Heteroskedastic Tensor Clustering
Tensor clustering, which seeks to extract underlying cluster structures from noisy tensor observations, has gained increasing attention.
Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans?
Vision-Language Models (VLMs) are trained on vast amounts of data captured by humans emulating our understanding of the world.
Efficiently Visualizing Large Graphs
Performing SPLEE to obtain a high-dimensional embedding of the large-scale graph and then using t-SGNE to reduce its dimension for visualization, we are able to visualize graphs with up to 300K nodes and 1M edges within 5 minutes and achieve approximately 10% improvement in visualization quality.
kTrans: Knowledge-Aware Transformer for Binary Code Embedding
By feeding explicit knowledge as additional inputs to the Transformer, and fusing implicit knowledge with a novel pre-training task, kTrans provides a new perspective to incorporating domain knowledge into a Transformer framework.
Quantifying the Roles of Visual, Linguistic, and Visual-Linguistic Complexity in Verb Acquisition
Based on these results, we conclude that verb acquisition is influenced by all three sources of complexity, but that the variability of visual structure poses the most significant challenge for verb learning.
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA
This paper is concerned with estimating the column subspace of a low-rank matrix $\boldsymbol{X}^\star \in \mathbb{R}^{n_1\times n_2}$ from contaminated data.
Deep Differential Amplifier for Extractive Summarization
In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework.
Inference for Low-rank Tensors -- No Need to Debias
In all these models, we observe that different from many matrix/vector settings in existing work, debiasing is not required to establish the asymptotic distribution of estimates or to make statistical inference on low-rank tensors.
Optimal High-order Tensor SVD via Tensor-Train Orthogonal Iteration
This paper studies a general framework for high-order tensor SVD.
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation
We show that this algorithm has an approximation ratio of $O((k+1)^{1/p})$ for $1\le p\le 2$ and $O((k+1)^{1-1/p})$ for $p\ge 2$.
Optimal Analysis of Subset-Selection Based L_p Low Rank Approximation
We show that this algorithm has an approximation ratio of $O((k+1)^{1/p})$ for $1\le p\le 2$ and $O((k+1)^{1-1/p})$ for $p\ge 2$.
Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference
We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse.
On the Non-asymptotic and Sharp Lower Tail Bounds of Random Variables
The non-asymptotic tail bounds of random variables play crucial roles in probability, statistics, and machine learning.
Dropout Training, Data-dependent Regularization, and Generalization Bounds
We study the problem of generalization guarantees for dropout training.
