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SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
In this paper we introduce a Transformer-based approach to video object ...
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Building LEGO Using Deep Generative Models of Graphs
Generative models are now used to create a variety of high-quality digit...
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Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization
Neural combinatorial optimization (NCO) aims at designing problem-indepe...
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Identifying and interpreting tuning dimensions in deep networks
In neuroscience, a tuning dimension is a stimulus attribute that account...
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Instance Selection for GANs
Recent advances in Generative Adversarial Networks (GANs) have led to th...
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Generative Graph Perturbations for Scene Graph Prediction
Inferring objects and their relationships from an image is useful in man...
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Enabling Continual Learning with Differentiable Hebbian Plasticity
Continual learning is the problem of sequentially learning new tasks or ...
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Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
Scene graph generation (SGG) aims to predict graph-structured descriptio...
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Sample-Efficient Model-based Actor-Critic for an Interactive Dialogue Task
Human-computer interactive systems that rely on machine learning are bec...
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ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
We consider the problem of distance metric learning (DML), where the tas...
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Learning with less data via Weakly Labeled Patch Classification in Digital Pathology
In Digital Pathology (DP), labeled data is generally very scarce due to ...
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Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking
Deep neural networks require collecting and annotating large amounts of ...
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A Nonparametric Bayesian Model for Sparse Temporal Multigraphs
As the availability and importance of temporal interaction data–such as ...
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Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions
Graphs evolving over time are a natural way to represent data in many do...
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Image Classification with Hierarchical Multigraph Networks
Graph Convolutional Networks (GCNs) are a class of general models that c...
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On the Evaluation of Conditional GANs
Conditional Generative Adversarial Networks (cGANs) are finding increasi...
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Understanding attention in graph neural networks
We aim to better understand attention over nodes in graph neural network...
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Batch Normalization is a Cause of Adversarial Vulnerability
Batch normalization (batch norm) is often used in an attempt to stabiliz...
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Similarity Learning Networks for Animal Individual Re-Identification - Beyond the Capabilities of a Human Observer
The ability of a researcher to re-identify (re-ID) an animal individual ...
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SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells
Objective: Lung cancer is the leading cause of cancer-related death worl...
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Adversarial Examples as an Input-Fault Tolerance Problem
We analyze the adversarial examples problem in terms of a model's fault ...
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Keep Drawing It: Iterative language-based image generation and editing
Conditional text-to-image generation approaches commonly focus on genera...
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Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
Spectral Graph Convolutional Networks (GCNs) are a generalization of con...
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Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data
The ability of a researcher to re-identify (re-ID) an individual animal ...
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Self-Paced Learning with Adaptive Deep Visual Embeddings
Selecting the most appropriate data examples to present a deep neural ne...
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Stochastic Layer-Wise Precision in Deep Neural Networks
Low precision weights, activations, and gradients have been proposed as ...
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Leveraging Uncertainty Estimates for Predicting Segmentation Quality
The use of deep learning for medical imaging has seen tremendous growth ...
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Adversarial Training Versus Weight Decay
Performance-critical machine learning models should be robust to input p...
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Deep Learning Object Detection Methods for Ecological Camera Trap Data
Deep learning methods for computer vision tasks show promise for automat...
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Generalized Hadamard-Product Fusion Operators for Visual Question Answering
We propose a generalized class of multimodal fusion operators for the ta...
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Real-Time End-to-End Action Detection with Two-Stream Networks
Two-stream networks have been very successful for solving the problem of...
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Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
We propose a method for human activity recognition from RGB data which d...
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Learning Confidence for Out-of-Distribution Detection in Neural Networks
Modern neural networks are very powerful predictive models, but they are...
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Predicting Adversarial Examples with High Confidence
It has been suggested that adversarial examples cause deep learning mode...
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Attacking Binarized Neural Networks
Neural networks with low-precision weights and activations offer compell...
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Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy
Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown con...
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Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction
Deep learning has been shown to outperform traditional machine learning ...
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Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels
A popular testbed for deep learning has been multimodal recognition of h...
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Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an a...
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Dataset Augmentation in Feature Space
Dataset augmentation, the practice of applying a wide array of domain-sp...
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An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning
Deep learning is an established framework for learning hierarchical data...
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Modeling Grasp Motor Imagery through Deep Conditional Generative Models
Grasping is a complex process involving knowledge of the object, the sur...
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Learning a metric for class-conditional KNN
Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework...
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Deep Learning on FPGAs: Past, Present, and Future
The rapid growth of data size and accessibility in recent years has inst...
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ModDrop: adaptive multi-modal gesture recognition
We present a method for gesture detection and localisation based on mult...
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Neural Network Regularization via Robust Weight Factorization
Regularization is essential when training large neural networks. As deep...
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Scoring and Classifying with Gated Auto-encoders
Auto-encoders are perhaps the best-known non-probabilistic methods for r...
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"Mental Rotation" by Optimizing Transforming Distance
The human visual system is able to recognize objects despite transformat...
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Learning Human Pose Estimation Features with Convolutional Networks
This paper introduces a new architecture for human pose estimation using...
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Products of Hidden Markov Models: It Takes N>1 to Tango
Products of Hidden Markov Models(PoHMMs) are an interesting class of gen...
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