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Torch-Struct: Deep Structured Prediction Library
The literature on structured prediction for NLP describes a rich collect...
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AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets
Recent studies have discovered the vulnerability of Deep Neural Networks...
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AutoML using Metadata Language Embeddings
As a human choosing a supervised learning algorithm, it is natural to be...
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Rational neural networks
We consider neural networks with rational activation functions. The choi...
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Cascaded Text Generation with Markov Transformers
The two dominant approaches to neural text generation are fully autoregr...
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Mixed Reinforcement Learning with Additive Stochastic Uncertainty
Reinforcement learning (RL) methods often rely on massive exploration da...
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Why is Attention Not So Attentive?
Attention-based methods have played an important role in model interpret...
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Blind Backdoors in Deep Learning Models
We investigate a new method for injecting backdoors into machine learnin...
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Neural Manifold Ordinary Differential Equations
To better conform to data geometry, recent deep generative modelling tec...
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Convolutional Networks with Dense Connectivity
Recent work has shown that convolutional networks can be substantially d...
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A Simple Baseline for Bayesian Uncertainty in Deep Learning
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose ...
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Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
Detecting objects such as cars and pedestrians in 3D plays an indispensa...
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When Does Self-supervision Improve Few-shot Learning?
We present a technique to improve the generalization of deep representat...
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Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning
In real world applications like healthcare, it is usually difficult to b...
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Blameworthiness in Multi-Agent Settings
We provide a formal definition of blameworthiness in settings where mult...
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Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
In the domain of autonomous driving, deep learning has substantially imp...
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Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation
Federated learning is a setting where agents, each with access to their ...
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CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
The ability to perform offline A/B-testing and off-policy learning using...
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Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
3D object detection is an essential task in autonomous driving. Recent t...
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Confidence Calibration for Convolutional Neural Networks Using Structured Dropout
In classification applications, we often want probabilistic predictions ...
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Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
In this paper, we propose a fast surrogate leverage weighted sampling st...
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3D human pose estimation with adaptive receptive fields and dilated temporal convolutions
In this work, we demonstrate that receptive fields in 3D pose estimation...
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Detecting and Tracking Communal Bird Roosts in Weather Radar Data
The US weather radar archive holds detailed information about biological...
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Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Classical deformable registration techniques achieve impressive results ...
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Efficient Rollout Strategies for Bayesian Optimization
Bayesian optimization (BO) is a class of sample-efficient global optimiz...
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Unsupervised Data Imputation via Variational Inference of Deep Subspaces
A wide range of systems exhibit high dimensional incomplete data. Accura...
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Robust Local Features for Improving the Generalization of Adversarial Training
Adversarial training has been demonstrated as one of the most effective ...
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Efficient Policy Learning from Surrogate-Loss Classification Reductions
Recent work on policy learning from observational data has highlighted t...
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Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests
A contextual bandit problem is studied in a highly non-stationary enviro...
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Low-volatility Anomaly and the Adaptive Multi-Factor Model
The paper explains the low-volatility anomaly from a new perspective. We...
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Low Frequency Adversarial Perturbation
Recently, machine learning security has received significant attention. ...
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Neural Naturalist: Generating Fine-Grained Image Comparisons
We introduce the new Birds-to-Words dataset of 41k sentences describing ...
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Aligning Superhuman AI and Human Behavior: Chess as a Model System
As artificial intelligence becomes increasingly intelligent—in some case...
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Decomposition of Longitudinal Deformations via Beltrami Descriptors
We present a mathematical model to decompose a longitudinal deformation ...
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Retouchdown: Adding Touchdown to StreetLearn as a Shareable Resource for Language Grounding Tasks in Street View
The Touchdown dataset (Chen et al., 2019) provides instructions by human...
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MOReL : Model-Based Offline Reinforcement Learning
In offline reinforcement learning (RL), the goal is to learn a successfu...
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Smoothed Analysis of Online and Differentially Private Learning
Practical and pervasive needs for robustness and privacy in algorithms h...
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Autonomous synthesis of metastable materials
Autonomous experimentation enabled by artificial intelligence (AI) offer...
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Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
Identifying changes in model parameters is fundamental in machine learni...
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Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short ...
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Disentangling Influence: Using Disentangled Representations to Audit Model Predictions
Motivated by the need to audit complex and black box models, there has b...
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Fair Learning-to-Rank from Implicit Feedback
Addressing unfairness in rankings has become an increasingly important p...
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On the Distribution of Minima in Intrinsic-Metric Rotation Averaging
Rotation Averaging is a non-convex optimization problem that determines ...
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Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition
Few-shot, fine-grained classification requires a model to learn subtle, ...
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An Auto-Encoder Strategy for Adaptive Image Segmentation
Deep neural networks are powerful tools for biomedical image segmentatio...
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GeoStyle: Discovering Fashion Trends and Events
Understanding fashion styles and trends is of great potential interest t...
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Boosting Supervision with Self-Supervision for Few-shot Learning
We present a technique to improve the transferability of deep representa...
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End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
Reliable and accurate 3D object detection is a necessity for safe autono...
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Stable Reinforcement Learning with Unbounded State Space
We consider the problem of reinforcement learning (RL) with unbounded st...
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A Markov Decision Process Approach to Active Meta Learning
In supervised learning, we fit a single statistical model to a given dat...
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