BAIR/CPAR/BDD Internal Weekly Seminar

The Berkeley Artificial Intelligence Research Lab co-hosts a weekly internal seminar series with the CITRIS People and Robots Initiative and the Berkeley Deep Drive Consortium. The seminars are every Friday afternoon in room 250 Sutardja Dai Hall from 3:10-4:10 PM, and are open to BAIR faculty, students, and sponsors. 


Fall 2017 Schedule
Date Speaker 1: 3:10-3:40 Speaker 2: 3:40-4:10
Aug. 25 Jitendra Malik Angjoo Kanazawa: Single-View 3D Reconstruction of Deformable Objects like Animals and People
Sept. 1 Chang Liu: Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection Claire Tomlin: Safe Learning
Sept. 8 Andrew Critch: Open-source game theory is weird Anna Rohrbach: Generation and grounding of natural language descriptions for visual data
Sept. 15 Bo Li: Secure learning in adversarial environments Ching-Yao Chan: Safety of Automated Driving Systems (ADS) and AI/ML – A Dialogue
Sept. 22 Alex Anderson: The High-Dimensional Geometry of Binary Neural Networks Jeff Mahler: Learning Deep Policies for Robot Bin Picking using Discrete-Event Simulation of Robust Grasping Sequences
Sept. 29 Fisher Yu: Towards Universal Representation for Image Recognition Max Rabinovich: Abstract Syntax Networks for Code Generation and Semantic Parsing
Oct. 6 Michael Laskey: Learning Home Robotics Manipulation Tasks from Remote Supervision Pulkit Agrawal: Continually Evolving Agents: Curiosity & Experimentation
Oct. 13 Ron Fearing: Dextrous Locomotion Sylvia Herbert and David Fridovich: Meta-Planning using FaSTrack for Fast and Safe Motion Planning
Oct. 20 Emrah Bostan: Learning Convex Regularizers for Optimal Bayesian Denoising Somil Bansal
Oct. 27 Roy Fox Aaditya Ramdas: Sequential testing and online false discovery rate control
Nov. 3 Roberto Calandra Leila Wehbe: Modeling brain responses to natural language stimuli
Nov. 10 Holiday Holiday
Nov. 17 Deepak Pathak tbd
Nov. 24 Holiday Holiday
Dec. 1 Eric Jonas Tuomas Haarnoja: Soft Q-Learning
Dec. 8 tbd tbd


Spring 2017 Schedule


Speaker 1: 3:10-3:40 Speaker 2: 3:40-4:10

Jan. 20

Jack Gallant: Melding neuroscience and computer science Javad Lavaei: On optimization theory, numerical algorithms and machine learning for nationwide energy systems

Jan. 27

Trevor Darrell: Adaptive Learning of Driving Models from Large-scale Video Datasets Coline Devin: Transfer learning for robotics

Feb. 3

Phillip Isola: Image-to-Image Translation with Conditional Adversarial Networks Anil Aswani: Human Modeling Using Inverse Optimization

Feb. 10

Zeynep Akata: Generating Fine-Grained Visual Explanations and Realistic Images Deirdre Mulligan: Hand-offs in AI: Functional Fidelity, Values, and Governance

Feb. 17

(3:10- 3:25) Jaime Fernandez Fisac: Generating plans that predict themselves/ (3:25-3:40) Aaron Bestick: Implicitly Assisting Humans to Choose Good Grasps in Robot to Human Handovers William Guss: WaveLayers: Neural Topology Inspired by Topology

Feb. 24

Laurent El Ghaoui: Safe feature elimination in sparse learning Laura Waller: Computational Microscopy with sparsity

Mar. 3

Chi Jin: How to Escape Saddle Points Efficiently Horia Mania: Universality of Mallows’ and degeneracy of Kendall’s kernels for rankings

Mar. 10

Matthew Matl: Automated Grasp Transfer with Mesh Segmentation and Point Cloud Registration Paul Grigas: An Extended Frank-Wolfe Method with “In-Face” Directions, and its Application to Low-Rank Matrix Completion

Mar. 17

Roy Fox: Multi-Level Discovery of Deep Options Parvez Ahammad: Beyond navigation metrics: Teaching computers how users perceive web application performance
Mar. 24 Ke LiLearning to Optimize and Fast k-Nearest Neighbour Search Jessica Hamrick: Metacontrol for Imagination-Based Optimization
Apr. 7 Deepak Pathak: Exploring Four Axes of Self-Supervision Saurabh GuptaCognitive Mapping and Planning for Visual Navigation
Apr. 14 Jeff Mahler: Dex-Net: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics Judy Hoffman: A General Framework for Domain Adversarial Learning
Apr. 21 Fereshteh Sadeghi: Sim2Real Collision Avoidance for Indoor Navigation of Mobile Robots via Deep Reinforcement Learning Evan Shelhamer: Loss is its own Reward: Self-Supervision for Reinforcement Learning
Apr. 28 Roberto Calandra: Robustness in Multi-objective Bayesian optimization Richard Zhang: Cross-Channel Visual Prediction
May 5 Abhishek Gupta: Imitation Learning for Dexterous Manipulation and Transfer in Reinforcement Learning Tinghui Zhou: 3D Visual Synthesis and Understanding from 2D Views
May 12 Karl Zipser: New Trajectories in the Autonomous Model Car Project Daniel Drew: Silent Swarms: Flying Microrobots Using Atmospheric Ion Thrusters


Fall 2016 Schedule


Speaker 1: 3:10-3:40 Speaker 2: 3:40-4:10

Aug. 26

Ken Goldberg: Deep Dexterity Sanjay Krishnan: Inverse Reinforcement Learning For Sequential Robotic Tasks

Sep 2

Anca Dragan: How Robots Influence Our Actions Jeff Donahue: Adversarial Feature Learning

Sep 9

Robert Nishihara & Philipp Moritz: Distributed Machine Learning with Ray John DeNero: Interactive machine translation

Sep 16

Joseph Gonzalez: Prediction Serving and RISE Kevin Jamieson: Bayesian Optimization and other bad ideas for hyperparameter tuning

Sep 23

Stuart Russell: Human-Compatible AI Marcus Rohrbach: Explain and Answer: Intelligent systems which can communicate about what they see.

Sep 30

Richard Zhang: Colorful Image Colorization Virginia Smith: A General Framework for Communication-Efficient Distributed Optimization

Oct 7

Gregory Kahn: Learning Control Policies for Partially Observable Safety-Critical Systems John Canny: Accountable Deep Networks

Oct 14

Ruzena Bajcsy: Individualized Human Models for Cyberphysical Interaction Jacob Andreas: Neural module networks

Oct 21

Dave Moore: Bayesian seismic monitoring from raw waveforms Greg Durrett: Data-Driven Text Analysis with Joint Models

Oct 28

Jun-Yan Zhu: Visual Manipulation and Synthesis on the Natural Image Manifold Dylan Hadfield-Menell: The Off-Switch

Nov 4

Bin Yu: Artificial neurons meet real neurons: building stable interpretations of V4 neurons from CNN+regression models Pulkit Agrawal: Forecasting from Pixels: Intuitive Physics and Intuitive Behavior

Dec 02

Chelsea Finn: Adversarial Inverse Reinforcement Learning Aviv Tamar: Deep Policy Representations based on a Planning Computation

Dec 09

Michael Laskey: A Human-Centric Approach to Deep Robotic Learning from Demonstrations. Michael Oliver: Using artificial neural networks to model visual neurons

Dec 16

Lisa Anne Hendricks: Localizing Moments in Video with Natural Language Andrew Owens: Learning visual models from paired audio-visual examples

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