California Institute of Technology

∙ Pasadena , United States ∙ 0 followers

The California Institute of Technology is a private doctorate-granting research university located in Pasadena, California, United States.

Implicit competitive regularization in GANs

Generative adversarial networks (GANs) are capable of producing high qua...
10/13/2019 ∙ by Florian Schäfer, et al. ∙ 49 ∙ share

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MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle flow algorithm may b...
01/21/2021 ∙ by Joosep Pata, et al. ∙ 41 ∙ share

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Out-of-Distribution Detection Using Neural Rendering Generative Models

Out-of-distribution (OoD) detection is a natural downstream task for dee...
07/10/2019 ∙ by Yujia Huang, et al. ∙ 35 ∙ share

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FERAtt: Facial Expression Recognition with Attention Net

We present a new end-to-end network architecture for facial expression r...
02/08/2019 ∙ by Pedro D. Marrero Fernandez, et al. ∙ 28 ∙ share

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A Framework for Machine Learning of Model Error in Dynamical Systems

The development of data-informed predictive models for dynamical systems...
07/14/2021 ∙ by Matthew E. Levine, et al. ∙ 27 ∙ share

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Learning Calibratable Policies using Programmatic Style-Consistency

We study the important and challenging problem of controllable generatio...
10/02/2019 ∙ by Eric Zhan, et al. ∙ 21 ∙ share

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Learning Differentiable Programs with Admissible Neural Heuristics

We study the problem of learning differentiable functions expressed as p...
07/23/2020 ∙ by Ameesh Shah, et al. ∙ 20 ∙ share

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Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

Learning-based control algorithms require collection of abundant supervi...
05/09/2020 ∙ by Yashwanth Kumar Nakka, et al. ∙ 19 ∙ share

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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...
05/02/2019 ∙ by Imad Alhousseini, et al. ∙ 18 ∙ share

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Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications

Trained on large datasets, deep learning (DL) can accurately classify vi...
03/03/2020 ∙ by Biagio Brattoli, et al. ∙ 17 ∙ share

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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...
12/28/2019 ∙ by Keng Wah Loon, et al. ∙ 16 ∙ share

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Plug and Play Language Models: a Simple Approach to Controlled Text Generation

Large transformer-based language models (LMs) trained on huge text corpo...
12/04/2019 ∙ by Sumanth Dathathri, et al. ∙ 13 ∙ share

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A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes

How can we efficiently gather information to optimize an unknown functio...
11/02/2018 ∙ by Jialin Song, et al. ∙ 12 ∙ share

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A General Method for Amortizing Variational Filtering

We introduce the variational filtering EM algorithm, a simple, general-p...
11/13/2018 ∙ by Joseph Marino, et al. ∙ 12 ∙ share

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Deep Metric Structured Learning For Facial Expression Recognition

We propose a deep metric learning model to create embedded sub-spaces wi...
01/18/2020 ∙ by Pedro D. Marrero Fernandez, et al. ∙ 12 ∙ share

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Risk-Averse Planning Under Uncertainty

We consider the problem of designing policies for partially observable M...
09/27/2019 ∙ by Mohamadreza Ahmadi, et al. ∙ 11 ∙ share

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HMM-guided frame querying for bandwidth-constrained video search

We design an agent to search for frames of interest in video stored on a...
12/31/2019 ∙ by Bhairav Chidambaram, et al. ∙ 11 ∙ share

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The Random Feature Model for Input-Output Maps between Banach Spaces

Well known to the machine learning community, the random feature model, ...
05/20/2020 ∙ by Nicholas H. Nelsen, et al. ∙ 11 ∙ share

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Asymptotic Escape of Spurious Critical Points on the Low-rank Matrix Manifold

We show that the Riemannian gradient descent algorithm on the low-rank m...
07/20/2021 ∙ by Thomas Y. Hou, et al. ∙ 11 ∙ share

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Using Data Imputation for Signal Separation in High Contrast Imaging

To characterize circumstellar systems in high contrast imaging, the fund...
01/02/2020 ∙ by Bin Ren, et al. ∙ 10 ∙ share

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Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs)...
07/08/2020 ∙ by Serim Ryou, et al. ∙ 10 ∙ share

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Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods

The increasing availability of data presents an opportunity to calibrate...
04/07/2021 ∙ by Andrew B. Duncan, et al. ∙ 10 ∙ share

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Geometric algorithms for predicting resilience and recovering damage in neural networks

Biological neural networks have evolved to maintain performance despite ...
05/23/2020 ∙ by Guruprasad Raghavan, et al. ∙ 9 ∙ share

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Consistent Robust Adversarial Prediction for General Multiclass Classification

We propose a robust adversarial prediction framework for general multicl...
12/18/2018 ∙ by Rizal Fathony, et al. ∙ 8 ∙ share

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ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification

African elephants are vital to their ecosystems, but their populations a...
06/29/2021 ∙ by Peter Kulits, et al. ∙ 8 ∙ share

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The GeoLifeCLEF 2020 Dataset

Understanding the geographic distribution of species is a key concern in...
04/08/2020 ∙ by Elijah Cole, et al. ∙ 7 ∙ share

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A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality

Stochastic mirror descent (SMD) is a fairly new family of algorithms tha...
04/03/2019 ∙ by Navid Azizan, et al. ∙ 6 ∙ share

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Kernel Analog Forecasting: Multiscale Test Problems

Data-driven prediction is becoming increasingly widespread as the volume...
05/13/2020 ∙ by Dmitry Burov, et al. ∙ 5 ∙ share

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Recognition in Terra Incognita

It is desirable for detection and classification algorithms to generaliz...
07/13/2018 ∙ by Sara Beery, et al. ∙ 4 ∙ share

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Analysis Of Momentum Methods

Gradient decent-based optimization methods underpin the parameter traini...
06/10/2019 ∙ by Nikola B. Kovachki, et al. ∙ 4 ∙ share

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Approximate Causal Abstraction

Scientific models describe natural phenomena at different levels of abst...
06/27/2019 ∙ by Sander Beckers, et al. ∙ 4 ∙ share

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Regret-optimal Estimation and Control

We consider estimation and control in linear time-varying dynamical syst...
06/22/2021 ∙ by Gautam Goel, et al. ∙ 4 ∙ share

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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...
06/21/2019 ∙ by Joey Hong, et al. ∙ 3 ∙ share

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Kernel Mode Decomposition and programmable/interpretable regression networks

Mode decomposition is a prototypical pattern recognition problem that ca...
07/19/2019 ∙ by Houman Owhadi, et al. ∙ 3 ∙ share

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Open Vocabulary Learning on Source Code with a Graph-Structured Cache

Machine learning models that take computer program source code as input ...
10/18/2018 ∙ by Milan Cvitkovic, et al. ∙ 2 ∙ share

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ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions

In recent years the possibility of relaxing the so-called Faithfulness a...
06/06/2019 ∙ by Zhalama, et al. ∙ 2 ∙ share

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Distributionally Robust Learning for Unsupervised Domain Adaptation

We propose a distributionally robust learning (DRL) method for unsupervi...
10/08/2020 ∙ by Haoxuan Wang, et al. ∙ 2 ∙ share

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Robust Regression for Safe Exploration in Control

We study the problem of safe learning and exploration in sequential cont...
06/13/2019 ∙ by Anqi Liu, et al. ∙ 1 ∙ share

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Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces

We refine the OrbNet model to accurately predict energy, forces, and oth...
11/05/2020 ∙ by Zhuoran Qiao, et al. ∙ 1 ∙ share

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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 ...
04/15/2016 ∙ by Stephan Zheng, et al. ∙ 0 ∙ share

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Quantifying Performance of Bipedal Standing with Multi-channel EMG

Spinal cord stimulation has enabled humans with motor complete spinal co...
11/21/2017 ∙ by Yanan Sui, et al. ∙ 0 ∙ share

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A Parallelizable Acceleration Framework for Packing Linear Programs

This paper presents an acceleration framework for packing linear program...
11/17/2017 ∙ by Palma London, et al. ∙ 0 ∙ share

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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...
10/07/2017 ∙ by Kun Li, et al. ∙ 0 ∙ share

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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...
09/22/2017 ∙ by Lawrence Stewart, et al. ∙ 0 ∙ share

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SEGMENT3D: A Web-based Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem

The quantitative analysis of 3D confocal microscopy images of the shoot ...
10/26/2017 ∙ by Thiago V. Spina, et al. ∙ 0 ∙ share

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Effective Image Differencing with ConvNets for Real-time Transient Hunting

Large sky surveys are increasingly relying on image subtraction pipeline...
10/04/2017 ∙ by Nima Sedaghat, et al. ∙ 0 ∙ share

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Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

Climate projections continue to be marred by large uncertainties, which ...
08/31/2017 ∙ by Tapio Schneider, et al. ∙ 0 ∙ share

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Deep-Learnt Classification of Light Curves

Astronomy light curves are sparse, gappy, and heteroscedastic. As a resu...
09/19/2017 ∙ by Ashish Mahabal, et al. ∙ 0 ∙ share

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On the enumeration of sentences by compactness

Presented is a Julia meta-program that discovers compact theories from d...
06/15/2017 ∙ by Mark A. Stalzer, et al. ∙ 0 ∙ share

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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Spatiotemporal forecasting has various applications in neuroscience, cli...
07/06/2017 ∙ by Yaguang Li, et al. ∙ 0 ∙ share

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