Search Results for author:
Found 15 papers, 8 papers with code
On Pretraining Data Diversity for Self-Supervised Learning
We explore the impact of training with more diverse datasets, characterized by the number of unique samples, on the performance of self-supervised learning (SSL) under a fixed computational budget.
SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
We present SynthCLIP, a novel framework for training CLIP models with entirely synthetic text-image pairs, significantly departing from previous methods relying on real data.
From Categories to Classifier: Name-Only Continual Learning by Exploring the Web
Continual Learning (CL) often relies on the availability of extensive annotated datasets, an assumption that is unrealistically time-consuming and costly in practice.
Mindstorms in Natural Language-Based Societies of Mind
What should be the social structure of an NLSOM?
Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?
We revisit the common practice of evaluating adaptation of Online Continual Learning (OCL) algorithms through the metric of online accuracy, which measures the accuracy of the model on the immediate next few samples.
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github. com/camel-ai/camel.
Don't FREAK Out: A Frequency-Inspired Approach to Detecting Backdoor Poisoned Samples in DNNs
In this paper we investigate the frequency sensitivity of Deep Neural Networks (DNNs) when presented with clean samples versus poisoned samples.
Computationally Budgeted Continual Learning: What Does Matter?
Our conclusions are consistent in a different number of stream time steps, e. g., 20 to 200, and under several computational budgets.
Real-Time Evaluation in Online Continual Learning: A New Hope
We show that a simple baseline outperforms state-of-the-art CL methods under this evaluation, questioning the applicability of existing methods in realistic settings.
Look, Listen, and Attack: Backdoor Attacks Against Video Action Recognition
Although backdoor attacks have been extensively studied in the image domain, there are very few works that explore such attacks in the video domain, and they tend to conclude that image backdoor attacks are less effective in the video domain.
Generalizability of Adversarial Robustness Under Distribution Shifts
Recent progress in empirical and certified robustness promises to deliver reliable and deployable Deep Neural Networks (DNNs).
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions.
Ranked #3 on
3D Semantic Segmentation
on OpenTrench3D
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
We then introduce a new Anisotropic Reduction function into our Separable SA module and propose an Anisotropic Separable SA (ASSA) module that substantially increases the network's accuracy.
Ranked #33 on
3D Part Segmentation
on ShapeNet-Part
Check Your Other Door! Creating Backdoor Attacks in the Frequency Domain
Deep Neural Networks (DNNs) are ubiquitous and span a variety of applications ranging from image classification to real-time object detection.
On the Decision Boundaries of Neural Networks. A Tropical Geometry Perspective
This work tackles the problem of characterizing and understanding the decision boundaries of neural networks with piecewise linear non-linearity activations.
