Proceedings of the 1st Annual Conference on Robot Learning on 13-15 November 2017 Published as Volume 78 by the Proceedings of Machine Learning Research on 18 October 2017. Volume Edited by: Sergey Levine Vincent Vanhoucke Ken Goldberg Series Editors: Neil D. Lawrence Mark Reid
Write Your Research provides practical resources for early-career researchers in the School of Technology, University of Cambridge. The resources were commissioned by the Department of Chemical Engineering and Biotechnology (CEB). Most of the resources were written by Anthony Haynes. 'Using social media' was written by Aoife Brophy Haney. 'Using bibliographic software' was written by Irenee…
Multiple Instance Learning (mil) Is A Variation Of Supervised Learning Where A Single Class Label Is Assigned To A Bag Of Instances. In This Paper, We State The Mil Problem As Learning The Bernoulli Distribution Of The Bag Label Where The Bag Label Probability Is Fully Parameterized By Neural Networks. Furthermore, We Propose A Neural Network-based Permutation-invariant Aggregation Operator That Corresponds To The Attention Mechanism. Notably, An Application Of The Proposed Attention-based Operator Provides Insight Into The Contribution Of Each Instance To The Bag Label. We Show Empirically That Our Approach Achieves Comparable Performance To The Best Mil Methods On Benchmark Mil Datasets And It Outperforms Other Methods On A Mnist-based Mil Dataset And Two Real-life Histopathology Datasets Without Sacrificing Interpretability.
Interacting Systems Are Prevalent In Nature, From Dynamical Systems In Physics To Complex Societal Dynamics. The Interplay Of Components Can Give Rise To Complex Behavior, Which Can Often Be Explained Using A Simple Model Of The System's Constituent Parts. In This Work, We Introduce The Neural Relational Inference (nri) Model: An Unsupervised Model That Learns To Infer Interactions While Simultaneously Learning The Dynamics Purely From Observational Data. Our Model Takes The Form Of A Variational Auto-encoder, In Which The Latent Code Represents The Underlying Interaction Graph And The Reconstruction Is Based On Graph Neural Networks. In Experiments On Simulated Physical Systems, We Show That Our Nri Model Can Accurately Recover Ground-truth Interactions In An Unsupervised Manner. We Further Demonstrate That We Can Find An Interpretable Structure And Predict Complex Dynamics In Real Motion Capture And Sports Tracking Data.
Today I successfully submitted my first paper to arXiv! We've submitted this paper to a journal, but it hasn't been published yet, so we wanted to get a pre-print up before advertising the corresponding software packages. Unfortunately, the process of submitting to arXiv wasn't painless. Now that I've figured out some of the quirks, however, hopefully your…
SciRate is an open source rating and commenting system for arXiv preprints. Papers are upvoted and discussed by the community, and we sometimes play host to more in depth peer review.
In Model-based Reinforcement Learning, Generative And Temporal Models Of Environments Can Be Leveraged To Boost Agent Performance, Either By Tuning The Agent's Representations During Training Or Via Use As Part Of An Explicit Planning Mechanism. However, Their Application In Practice Has Been Limited To Simplistic Environments, Due To The Difficulty Of Training Such Models In Larger, Potentially Partially-observed And 3d Environments. In This Work We Introduce A Novel Action-conditioned Generative Model Of Such Challenging Environments. The Model Features A Non-parametric Spatial Memory System In Which We Store Learned, Disentangled Representations Of The Environment. Low-dimensional Spatial Updates Are Computed Using A State-space Model That Makes Use Of Knowledge On The Prior Dynamics Of The Moving Agent, And High-dimensional Visual Observations Are Modelled With A Variational Auto-encoder. The Result Is A Scalable Architecture Capable Of Performing Coherent Predictions Over Hundreds Of Time Steps Across A Range Of Partially Observed 2d And 3d Environments.
Writing a mathematical paper is both an act of recording mathematical content and a means of communication of one's work. In contrast with other types of writing, the style of math papers is incredibly rigid and resistant to even modest innovation. As a result, both goals suffer, sometimes immeasurably. The clarity suffers the most, which…
Advanced plagiarism checker and citation assistant with many professional features. Our proprietary DeepSearch™ technology checks for plagiarism better than any other technology.
I am fascinated by technology and its application to data analysis in finance, especially investing. Below is a compiled list of freely available academic papers published in 2017 on deep learning…
One of the most popular posts on the Thesis Whisperer is How to write 1000 words a day and not go bat shit crazy. Last year a Twitter follower brought to my attention a post called How I went from writing 2000 words to 10,000 words a day by the fiction writer Rachel Aaron. I…
S. Albrecht, and P. Stone. (2017)cite arxiv:1709.08071Comment: 42 pages, submitted for review to Artificial Intelligence Journal. Keywords: multiagent systems, agent modelling, opponent modelling, survey, open problems.
A. Boulch, and R. Marlet. Proceedings of the Symposium on Geometry Processing, page 281--290. Goslar Germany, Germany, Eurographics Association, (2016)
A. Chaabani, M. Bellamine, and M. Gasmi. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 3 (4):
17(November 2014)