Connectionists: SoCal Machine Learning Symposium - Nov 18 @ Caltech, registration deadline Nov 11

[Julian McAuley]

Reminder that the registration deadline for the Southern California Machine
Learning Symposium is *this Friday*, November 11.

http://dolcit.cms.caltech.edu/scmls/

We have 42 posters, three guest speakers, 6 sponsors, plus booths, swag,
and wonderful prizes. And catering! Registration is only $10. See the above
website for local information etc.

Hope to see you there soon!
Yisong Yue, Julian McAuley

===== Invited speakers and accepted submissions =====

Invited speakers:
Yan Liu (USC)
Silvio Savarese (Stanford)
Guy Van den Broeck (UCLA)

List of accepted submissions:
1. Low-rank Bilinear Pooling for Fine-Grained Classification (Shu Kong and
Charless Fowlkes)
2. Using Gaussian process models to predict channelrhodopsin plasma mebrane
localization (Kevin Yang)
3. Active Learning from Weak and Strong Labelers (Chicheng Zhang and
Kamalika Chaudhuri)
4. Mixed Membership Word Embeddings: Corpus-Specific Embeddings Without Big
Data (James Foulds)
5. Tensor Contractions with Extended BLAS Kernels on CPU and GPU (Yang Shi,
U N Niranjan, Animashree Anandkumar and Cris Cecka)
6. Exchange Rate Prediction from Twitter's Trending Topics (Fulya Ozcan)
7. Distribution-free Detection of a Submatrix (Ery Arias-Castro and Yuchao
Liu)
8. Modeling and Predicting the Performance of Electrical Spinal Cord
Stimulation in Patients with Spinal Cord Injury (Ellen Feldman and Joel
Burdick)
9. Circuits in the retina: Deep learning as a biological modeling tool
(Dawna Bagherian, Taehwan Kim, Yisong Yue and Markus Meister)
10. A simple approach to sparse clustering (Xiao Pu and Ery Arias-Castro)
11. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
(Lisha Li, Kevin Jamieson, Giulia Desalvo, Afshin Rostamizadeh and Ameet
Talwalkar)
12. Blending Spatial Modeling and Probabilistic Bisection (Sergio Rodriguez
and Michael Ludkovski)
13. Relation Extraction from BioMedical Texts using Convolution Neural
Networks (Ankit Goyal and Chun-Nan Hsu)
14. Distributed Bayesian Filtering Algorithm for Dynamic Sensor Networks
(Saptarshi Bandyopadhyay and Soon-Jo Chung)
15. Collaborative Filtering as a Case-Study for Model Parallelism on Bulk
Synchronous Systems (Ariyam Das, Ishan Upadhyaya, Xiangrui Meng and Ameet
Talwalkar)
16. A Performance Model for Training Deep Neural Networks (Hang Qi, Evan
Sparks and Ameet Talwalkar)
17. Numerical Results on Directed Graph Process Distances for Model
Architectures: Inter and Intra-Lineage (Eric Mjolsness and Cory Scott)
18. Gene Regulatory Network Modeling with Neural Network ODEs (Dustin
Maurer and Eric Mjolsness)
19. Faster Constraint Solving Using Learning Based Abstractions (Sumanth
Dathathri, Nikos Arechiga and Sicun Gao)
20. Active Long Term Memory Networks (Tommaso Furlanello, Jiaping Zhao,
Andrew Saxe, Laurent Itti and Bosco S. Tjan)
21. A Class of Neural-Based Dynamics for Online Learning of Incentive
Mechanisms in Congestion Games with Stochastic Switching Graphs (Jorge I.
Poveda, Philip N. Brown, Jason R. Marden and Andrew R. Teel)
22. Correlational Dueling Bandits with Application to Clinical Treatment in
Large Decision Space (Yanan Sui and Joel Burdick)
23. Dictionary Learning with Semidefinite Representations (Yong Sheng Soh
and Venkat Chandrasekaran)
24. Variational Adversarial Deep Domain Adaptation for Healthcare Time
Series (Sanjay Purushotham, Wilka Carvalho and Yan Liu)
25. Abstract: Robust Channel Coding Strategies for Machine Learning Data
(Kayvon Mazooji, Frederic Sala, Guy Van den Broeck and Lara Dolecek)
26. Bayesian Triplet Learning (Vicente Malave and Angela Yu)
27. Neural Network Compression With Tensors (Rose Yu and Yan Liu)
28. Boltzmann Chemical Reaction Networks (William Poole, Erik Winfree,
Abhishek Behera, Manoj Gopalkrishnan, Nick Jones, Tom Ouldridge and Andres
Ortiz-Munoz)
29. Performance Comparison between TRPO and CEM for Deep Reinforcement
Learning (Sebastien Arnold, Elizabeth Chu and Francisco Valero-Cuevas)
30. Change-Point Detection without Needing to Detect Change-Points?
(Chaitanya Ryali and Angela J. Yu)
31. A Semi-Supervised Machine Learning Approach for Healthcare Applications
(Mina Ch. Moghadam, Masoumeh Ebrahimi and Nader Bagherzadeh)
32. What You Ask Is What You Get: Query Design and Robust Algorithms for
Crowdsourced Clustering (Ramya Korlakai Vinayak and Babak Hassibi)
33. Associating Semantics to Latent Variables (Armeen Taeb and Venkat
Chandrasekaran)
34. Generating Long-term Trajectories Using Deep Hierarchical Networks
(Stephan Zheng and Yisong Yue)
35. A Rotation Invariant Latent Factor Model for Moveme Discovery from
Static Poses (Matteo Ruggero Ronchi, Joon Sik Kim and Yisong Yue)
36. Learning recurrent representations for dynamic behavior modeling (Eyrun
Eyjolfsdottir, Kristin Branson, Yisong Yue and Pietro Perona)
37. Stability of Causal Inference (Leonard Schulman and Piyush Srivastava)
38. Deciding how to decide: dynamic routing in artificial neural networks
(Mason McGill and Pietro Perona)
39. Data-Driven Ghosting using Deep Imitation Learning (Hoang Le)
40. Beyond LDA: A Unified Framework for Learning Latent Normalized
Infinitely Divisible Topic Models through Spectral Methods (Forough
Arabshahi and Animashree Anandkumar)
41. The Possibilities and Limitations of Private Prediction Markets (Rachel
Cummings)
42. Learning interpretable features of facial attractiveness (Amanda Song,
Linjie Li and Garrison Cottrell)
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