Search Results for author:
Found 12 papers, 3 papers with code
Complement Objective Training
Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes.
Improving Adversarial Robustness via Guided Complement Entropy
Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model.
AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval
Despite recent success in neural task-oriented dialogue systems, developing such a real-world system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory network mechanisms.
DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters
When will a server fail catastrophically in an industrial datacenter?
MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning
Recent studies on neural architecture search have shown that automatically designed neural networks perform as good as expert-crafted architectures.
Searching Toward Pareto-Optimal Device-Aware Neural Architectures
Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding.
Dynamic Early Terminating of Multiply Accumulate Operations for Saving Computation Cost in Convolutional Neural Networks
We propose to set a checkpoint in the MAC process to determine whether a filter could terminate early based on the intermediate result.
Learning with Hierarchical Complement Objective
Label hierarchies widely exist in many vision-related problems, ranging from explicit label hierarchies existed in image classification to latent label hierarchies existed in semantic segmentation.
Robust Processing-In-Memory Neural Networks via Noise-Aware Normalization
In this paper, we propose a noise-agnostic method to achieve robust neural network performance against any noise setting.
Remix: Rebalanced Mixup
In this work, we propose a new regularization technique, Remix, that relaxes Mixup's formulation and enables the mixing factors of features and labels to be disentangled.
Context-Aware Temperature for Language Modeling
Current practices to apply temperature scaling assume either a fixed, or a manually-crafted dynamically changing schedule.
Contextual Temperature for Language Modeling
Temperature scaling has been widely used as an effective approach to control the smoothness of a distribution, which helps the model performance in various tasks.
