Search Results for author:
Found 13 papers, 7 papers with code
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods.
Learning Phase Competition for Traffic Signal Control
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions.
Learning to Learn by Zeroth-Order Oracle
In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules.
Improved Adversarial Training via Learned Optimizer
Adversarial attack has recently become a tremendous threat to deep learning models.
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers
For end-to-end efficiency, unlike previous work that assumes random hyperparameter tuning, which over-emphasizes the tuning time, we propose to evaluate with a bandit hyperparameter tuning strategy.
Learning to Learn with Smooth Regularization
Recent decades have witnessed great prosperity of deep learning in tackling various problems such as classification and decision making.
Training Meta-Surrogate Model for Transferable Adversarial Attack
In this paper, we tackle this problem from a novel angle -- instead of using the original surrogate models, can we obtain a Meta-Surrogate Model (MSM) such that attacks to this model can be easier transferred to other models?
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.
Adversarial Attack across Datasets
In this paper, we define a Generalized Transferable Attack (GTA) problem where the attacker doesn't know this information and is acquired to attack any randomly encountered images that may come from unknown datasets.
Extreme Zero-Shot Learning for Extreme Text Classification
To learn the semantic embeddings of instances and labels with raw text, we propose to pre-train Transformer-based encoders with self-supervised contrastive losses.
Multi Label Text Classification
Multi-Label Text Classification
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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.
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding
Existing video-language pre-training methods primarily focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information in both videos and text, which is of importance to downstream tasks requiring temporal localization and semantic reasoning.
