Search Results for author:
Found 16 papers, 3 papers with code
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.
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.
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.
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.
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.
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.
Leveraging Motion Priors in Videos for Improving Human Segmentation
Recently, a few domain adaptation and active learning approaches have been proposed to mitigate the performance drop.
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.
Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs
We present a novel framework for finding complex activities matching user-described queries in cluttered surveillance videos.
DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters
When will a server fail catastrophically in an industrial datacenter?
No More Discrimination: Cross City Adaptation of Road Scene Segmenters
Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases.
Efficient Training of Very Deep Neural Networks for Supervised Hashing
In this paper, we propose training very deep neural networks (DNNs) for supervised learning of hash codes.
Group Membership Prediction
In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances.
A Novel Visual Word Co-occurrence Model for Person Re-identification
We first map each pixel of an image to a visual word using a codebook, which is learned in an unsupervised manner.
A Rank-SVM Approach to Anomaly Detection
The resulting anomaly detector is shown to be asymptotically optimal and adaptive in that for any false alarm rate alpha, its decision region converges to the alpha-percentile level set of the unknown underlying density.
