Search Results for author:
Found 11 papers, 9 papers with code
DrNAS: Dirichlet Neural Architecture Search
This paper proposes a novel differentiable architecture search method by formulating it into a distribution learning problem.
Rethinking Architecture Selection in Differentiable NAS
Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) methods for its search efficiency and simplicity, accomplished by jointly optimizing the model weight and architecture parameters in a weight-sharing supernet via gradient-based algorithms.
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving
Predictor-based algorithms have achieved remarkable performance in the Neural Architecture Search (NAS) tasks.
Learning to Schedule Learning rate with Graph Neural Networks
By constructing a directed graph for the underlying neural network of the target problem, GNS encodes current dynamics with a graph message passing network and trains an agent to control the learning rate accordingly via reinforcement learning.
Generalizing Few-Shot NAS with Gradient Matching
Efficient performance estimation of architectures drawn from large search spaces is essential to Neural Architecture Search.
DC-BENCH: Dataset Condensation Benchmark
Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset.
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
Federated learning~(FL) has recently attracted increasing attention from academia and industry, with the ultimate goal of achieving collaborative training under privacy and communication constraints.
Efficient Non-Parametric Optimizer Search for Diverse Tasks
Efficient and automated design of optimizers plays a crucial role in full-stack AutoML systems.
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
The resulting algorithm sets new SOTA on ImageNet-1K: we can scale up to 50 IPCs (Image Per Class) on ImageNet-1K on a single GPU (all previous methods can only scale to 2 IPCs on ImageNet-1K), leading to the best accuracy (only 5. 9% accuracy drop against full dataset training) while utilizing only 4. 2% of the number of data points - an 18. 2% absolute gain over prior SOTA.
MuLan: Multimodal-LLM Agent for Progressive Multi-Object Diffusion
Moreover, MuLan adopts a vision-language model (VLM) to provide feedback to the image generated in each sub-task and control the diffusion model to re-generate the image if it violates the original prompt.
DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers
DrAttack includes three key components: (a) `Decomposition' of the original prompt into sub-prompts, (b) `Reconstruction' of these sub-prompts implicitly by in-context learning with semantically similar but harmless reassembling demo, and (c) a `Synonym Search' of sub-prompts, aiming to find sub-prompts' synonyms that maintain the original intent while jailbreaking LLMs.
