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HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities
Multimodal datasets contain an enormous amount of relational information...
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Increasing Expressivity of a Hyperspherical VAE
Learning suitable latent representations for observed, high-dimensional ...
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Cooperative Embeddings for Instance, Attribute and Category Retrieval
The goal of this paper is to retrieve an image based on instance, attrib...
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How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking
Attribution methods assess the contribution of inputs (e.g., words) to t...
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Conditional Image Generation and Manipulation for User-Specified Content
In recent years, Generative Adversarial Networks (GANs) have improved st...
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Learning Likelihoods with Conditional Normalizing Flows
Normalizing Flows (NFs) are able to model complicated distributions p(y)...
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Learning to Rank from Samples of Variable Quality
Training deep neural networks requires many training samples, but in pra...
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VideoGraph: Recognizing Minutes-Long Human Activities in Videos
Many human activities take minutes to unfold. To represent them, related...
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Color Constancy by GANs: An Experimental Survey
In this paper, we formulate the color constancy task as an image-to-imag...
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TimeGate: Conditional Gating of Segments in Long-range Activities
When recognizing a long-range activity, exploring the entire video is ex...
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Physics-based Shading Reconstruction for Intrinsic Image Decomposition
We investigate the use of photometric invariance and deep learning to co...
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Block Neural Autoregressive Flow
Normalising flows (NFS) map two density functions via a differentiable b...
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Open Cross-Domain Visual Search
This paper introduces open cross-domain visual search, where categories ...
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SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Normalizing flows and variational autoencoders are powerful generative m...
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DIVA: Domain Invariant Variational Autoencoders
We consider the problem of domain generalization, namely, how to learn r...
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Probabilistic Binary Neural Networks
Low bit-width weights and activations are an effective way of combating ...
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Feedback Graph Convolutional Network for Skeleton-based Action Recognition
Skeleton-based action recognition has attracted considerable attention i...
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High-Performance Long-Term Tracking with Meta-Updater
Long-term visual tracking has drawn increasing attention because it is m...
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The Convolution Exponential and Generalized Sylvester Flows
This paper introduces a new method to build linear flows, by taking the ...
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PointMixup: Augmentation for Point Clouds
This paper introduces data augmentation for point clouds by interpolatio...
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RefNet: A Reference-aware Network for Background Based Conversation
Existing conversational systems tend to generate generic responses. Rece...
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Actor-Transformers for Group Activity Recognition
This paper strives to recognize individual actions and group activities ...
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Experimental design for MRI by greedy policy search
In today's clinical practice, magnetic resonance imaging (MRI) is routin...
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Combining Generative and Discriminative Models for Hybrid Inference
A graphical model is a structured representation of the data generating ...
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Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search
Recent research on conversational search highlights the importance of mi...
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A Layer-Based Sequential Framework for Scene Generation with GANs
The visual world we sense, interpret and interact everyday is a complex ...
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Integer Discrete Flows and Lossless Compression
Lossless compression methods shorten the expected representation size of...
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Universal Transformers
Self-attentive feed-forward sequence models have been shown to achieve i...
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Unsupervised Generation of Optical Flow Datasets from Videos in the Wild
Dense optical flow ground truth of non-rigid motion for real-world image...
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ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition
In general, intrinsic image decomposition algorithms interpret shading a...
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Joint Learning of Intrinsic Images and Semantic Segmentation
Semantic segmentation of outdoor scenes is problematic when there are va...
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Deep Scale-spaces: Equivariance Over Scale
We introduce deep scale-spaces (DSS), a generalization of convolutional ...
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Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
In many real-world scenarios, an autonomous agent often encounters vario...
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Improving Outfit Recommendation with Co-supervision of Fashion Generation
The task of fashion recommendation includes two main challenges: visual ...
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Social navigation with human empowerment driven reinforcement learning
The next generation of mobile robots needs to be socially-compliant to b...
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Repetition Estimation
Visual repetition is ubiquitous in our world. It appears in human activi...
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The Functional Neural Process
We present a new family of exchangeable stochastic processes, the Functi...
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Message Passing for Complex Question Answering over Knowledge Graphs
Question answering over knowledge graphs (KGQA) has evolved from simple ...
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SEntNet: Source-aware Recurrent Entity Network for Dialogue Response Selection
Dialogue response selection is an important part of Task-oriented Dialog...
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BubbleRank: Safe Online Learning to Rerank
We study the problem of online learning to re-rank, where users provide ...
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Measuring Semantic Coherence of a Conversation
Conversational systems have become increasingly popular as a way for hum...
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Textual Explanations for Self-Driving Vehicles
Deep neural perception and control networks have become key components o...
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Manipulating Attributes of Natural Scenes via Hallucination
In this study, we explore building a two-stage framework for enabling us...
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Relaxed Quantization for Discretized Neural Networks
Neural network quantization has become an important research area due to...
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Pixelated Semantic Colorization
While many image colorization algorithms have recently shown the capabil...
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Preferences Implicit in the State of the World
Reinforcement learning (RL) agents optimize only the features specified ...
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Explaining Predictions from Tree-based Boosting Ensembles
Understanding how "black-box" models arrive at their predictions has spa...
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RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection
In this paper, we present a novel neural network for MIMO symbol detecti...
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CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition
Most of the traditional work on intrinsic image decomposition rely on de...
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3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
We present a convolutional network that is equivariant to rigid body mot...
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