Search Results for author:
Found 12 papers, 6 papers with code
L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects
In this paper, we propose a language agent with chain-of-3D-thoughts (L3GO), an inference-time approach that can reason about part-based 3D mesh generation of unconventional objects that current data-driven diffusion models struggle with.
Evaluating Spatial Understanding of Large Language Models
Large language models (LLMs) show remarkable capabilities across a variety of tasks.
Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations
In this work, we evaluate LLM APIs (ChatGPT, GPT-3, and GPT-4) on the Japanese national medical licensing examinations from the past five years, including the current year.
When are Lemons Purple? The Concept Association Bias of Vision-Language Models
Large-scale vision-language models such as CLIP have shown impressive performance on zero-shot image classification and image-to-text retrieval.
Does Robustness on ImageNet Transfer to Downstream Tasks?
For object detection and semantic segmentation, we find that a vanilla Swin Transformer, a variant of Vision Transformer tailored for dense prediction tasks, transfers robustness better than Convolutional Neural Networks that are trained to be robust to the corrupted version of ImageNet.
Can Wikipedia Help Offline Reinforcement Learning?
In this paper, we look to take advantage of this formulation of reinforcement learning as sequence modeling and investigate the transferability of pre-trained sequence models on other domains (vision, language) when finetuned on offline RL tasks (control, games).
Support Recovery with Stochastic Gates: Theory and Application for Linear Models
Consider the problem of simultaneous estimation and support recovery of the coefficient vector in a linear data model with additive Gaussian noise.
Exploiting 3D Shape Bias towards Robust Vision
To tackle the problem from a new perspective, we encourage closer collaboration between the robustness and 3D vision communities.
Geon3D: Exploiting 3D Shape Bias towards Building Robust Machine Vision
To tackle the problem from a new perspective, we encourage closer collaboration between the robustness and 3D vision communities.
Feature Selection using Stochastic Gates
Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions.
Defending against Adversarial Images using Basis Functions Transformations
We study the effectiveness of various approaches that defend against adversarial attacks on deep networks via manipulations based on basis function representations of images.
Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization
We propose a general framework for increasing local stability of Artificial Neural Nets (ANNs) using Robust Optimization (RO).
