Search Results for author:
Found 16 papers, 5 papers with code
Where and when to look? Spatial-temporal attention for action recognition in videos
Our model is efficient, as it proposes a separable spatio-temporal mechanism for video attention, while being able to identify important parts of the video both spatially and temporally.
Meta Temporal Point Processes
In this work, we propose to train TPPs in a meta learning framework, where each sequence is treated as a different task, via a novel framing of TPPs as neural processes (NPs).
Gumbel-Softmax Selective Networks
This commonly encountered operational context calls for principled techniques for training ML models with the option to abstain from predicting when uncertain.
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
Data imbalance, in which a plurality of the data samples come from a small proportion of labels, poses a challenge in training deep neural networks.
DYNASHARE: DYNAMIC NEURAL NETWORKS FOR MULTI-TASK LEARNING
Parameter sharing approaches for deep multi-task learning share a common intuition: for a single network to perform multiple prediction tasks, the network needs to support multiple specialized execution paths.
Heterogeneous Multi-task Learning with Expert Diversity
In contrast to single-task learning, in which a separate model is trained for each target, multi-task learning (MTL) optimizes a single model to predict multiple related targets simultaneously.
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation
In contrast, we propose a parameter efficient framework, Piggyback GAN, which learns the current task by building a set of convolutional and deconvolutional filters that are factorized into filters of the models trained on previous tasks.
Learning Discriminative Prototypes with Dynamic Time Warping
We propose Discriminative Prototype DTW (DP-DTW), a novel method to learn class-specific discriminative prototypes for temporal recognition tasks.
Similarity-Preserving Knowledge Distillation
Knowledge distillation is a widely applicable technique for training a student neural network under the guidance of a trained teacher network.
Constraint-Aware Deep Neural Network Compression
Deep neural network compression has the potential to bring modern resource-hungry deep networks to resource-limited devices.
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization
This allows us to take advantage of the complementary nature of pruning and quantization and to recover from premature pruning errors, which is not possible with current two-stage approaches.
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection.
Multi-level Residual Networks from Dynamical Systems View
Deep residual networks (ResNets) and their variants are widely used in many computer vision applications and natural language processing tasks.
Backtracking Regression Forests for Accurate Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection.
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization
When approaching a novel visual recognition problem in a specialized image domain, a common strategy is to start with a pre-trained deep neural network and fine-tune it to the specialized domain.
Learning Person Trajectory Representations for Team Activity Analysis
Activity analysis in which multiple people interact across a large space is challenging due to the interplay of individual actions and collective group dynamics.
