
Implicit competitive regularization in GANs
Generative adversarial networks (GANs) are capable of producing high qua...
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MLPF: Efficient machinelearned particleflow reconstruction using graph neural networks
In generalpurpose particle detectors, the particle flow algorithm may b...
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OutofDistribution Detection Using Neural Rendering Generative Models
Outofdistribution (OoD) detection is a natural downstream task for dee...
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FERAtt: Facial Expression Recognition with Attention Net
We present a new endtoend network architecture for facial expression r...
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A Framework for Machine Learning of Model Error in Dynamical Systems
The development of datainformed predictive models for dynamical systems...
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Learning Calibratable Policies using Programmatic StyleConsistency
We study the important and challenging problem of controllable generatio...
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Learning Differentiable Programs with Admissible Neural Heuristics
We study the problem of learning differentiable functions expressed as p...
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ChanceConstrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
Learningbased control algorithms require collection of abundant supervi...
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Physicist's Journeys Through the AI World  A Topical Review. There is no royal road to unsupervised learning
Artificial Intelligence (AI), defined in its most simple form, is a tech...
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Rethinking Zeroshot Video Classification: Endtoend Training for Realistic Applications
Trained on large datasets, deep learning (DL) can accurately classify vi...
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SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning
We introduce SLM Lab, a software framework for reproducible reinforcemen...
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Plug and Play Language Models: a Simple Approach to Controlled Text Generation
Large transformerbased language models (LMs) trained on huge text corpo...
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A General Framework for Multifidelity Bayesian Optimization with Gaussian Processes
How can we efficiently gather information to optimize an unknown functio...
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A General Method for Amortizing Variational Filtering
We introduce the variational filtering EM algorithm, a simple, generalp...
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Deep Metric Structured Learning For Facial Expression Recognition
We propose a deep metric learning model to create embedded subspaces wi...
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RiskAverse Planning Under Uncertainty
We consider the problem of designing policies for partially observable M...
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HMMguided frame querying for bandwidthconstrained video search
We design an agent to search for frames of interest in video stored on a...
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The Random Feature Model for InputOutput Maps between Banach Spaces
Well known to the machine learning community, the random feature model, ...
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Asymptotic Escape of Spurious Critical Points on the Lowrank Matrix Manifold
We show that the Riemannian gradient descent algorithm on the lowrank m...
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Using Data Imputation for Signal Separation in High Contrast Imaging
To characterize circumstellar systems in high contrast imaging, the fund...
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Graph Neural Networks for the Prediction of SubstrateSpecific Organic Reaction Conditions
We present a systematic investigation using graph neural networks (GNNs)...
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Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods
The increasing availability of data presents an opportunity to calibrate...
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Geometric algorithms for predicting resilience and recovering damage in neural networks
Biological neural networks have evolved to maintain performance despite ...
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Consistent Robust Adversarial Prediction for General Multiclass Classification
We propose a robust adversarial prediction framework for general multicl...
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ElephantBook: A SemiAutomated HumanintheLoop System for Elephant ReIdentification
African elephants are vital to their ecosystems, but their populations a...
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The GeoLifeCLEF 2020 Dataset
Understanding the geographic distribution of species is a key concern in...
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A Stochastic Interpretation of Stochastic Mirror Descent: RiskSensitive Optimality
Stochastic mirror descent (SMD) is a fairly new family of algorithms tha...
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Kernel Analog Forecasting: Multiscale Test Problems
Datadriven prediction is becoming increasingly widespread as the volume...
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Recognition in Terra Incognita
It is desirable for detection and classification algorithms to generaliz...
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Analysis Of Momentum Methods
Gradient decentbased optimization methods underpin the parameter traini...
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Approximate Causal Abstraction
Scientific models describe natural phenomena at different levels of abst...
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Regretoptimal Estimation and Control
We consider estimation and control in linear timevarying dynamical syst...
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Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions
We focus on the problem of predicting future states of entities in compl...
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Kernel Mode Decomposition and programmable/interpretable regression networks
Mode decomposition is a prototypical pattern recognition problem that ca...
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Open Vocabulary Learning on Source Code with a GraphStructured Cache
Machine learning models that take computer program source code as input ...
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ASPbased Discovery of SemiMarkovian Causal Models under Weaker Assumptions
In recent years the possibility of relaxing the socalled Faithfulness a...
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Distributionally Robust Learning for Unsupervised Domain Adaptation
We propose a distributionally robust learning (DRL) method for unsupervi...
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Robust Regression for Safe Exploration in Control
We study the problem of safe learning and exploration in sequential cont...
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Multitask learning for electronic structure to predict and explore molecular potential energy surfaces
We refine the OrbNet model to accurately predict energy, forces, and oth...
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Improving the Robustness of Deep Neural Networks via Stability Training
In this paper we address the issue of output instability of deep neural ...
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Quantifying Performance of Bipedal Standing with Multichannel EMG
Spinal cord stimulation has enabled humans with motor complete spinal co...
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A Parallelizable Acceleration Framework for Packing Linear Programs
This paper presents an acceleration framework for packing linear program...
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Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human Motion Analysis
This work handles the inverse reinforcement learning (IRL) problem where...
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Bayesian Optimization for Parameter Tuning of the XOR Neural Network
When applying Machine Learning techniques to problems, one must select m...
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SEGMENT3D: A Webbased Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem
The quantitative analysis of 3D confocal microscopy images of the shoot ...
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Effective Image Differencing with ConvNets for Realtime Transient Hunting
Large sky surveys are increasingly relying on image subtraction pipeline...
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Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted HighResolution Simulations
Climate projections continue to be marred by large uncertainties, which ...
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DeepLearnt Classification of Light Curves
Astronomy light curves are sparse, gappy, and heteroscedastic. As a resu...
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On the enumeration of sentences by compactness
Presented is a Julia metaprogram that discovers compact theories from d...
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Diffusion Convolutional Recurrent Neural Network: DataDriven Traffic Forecasting
Spatiotemporal forecasting has various applications in neuroscience, cli...
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California Institute of Technology
The California Institute of Technology is a private doctorategranting research university located in Pasadena, California, United States.