Quest Symposium on Robust, Interpretable
Deep Learning Systems
Posters
Poster format: up to 48''×72'', paper.
Set up posters at 2:00pm before the event.
| Number | Poster Title | Name |
|---|---|---|
| A01 | On Algorithms for Adversarial Dynamics | Abdullah Al-Dujaili |
| A02 | Defense Against Adversarial Attacks using Web-Scale Nearest Neighbors Search | Abhimanyu Dubey |
| A03 | Clean-Label Backdoor Attacks | Alexander Turner |
| A04 | Generally Exciting Inputs and How to Get Rid Of Them: A Little Network Introspection | Aspen |
| A05 | Combining Machine Learning with Deductive Reasoning for Improved Explainability | Ben Z Yuan |
| A06 | Safe Reinforcement Learning with Model Uncertainty Estimates for Dynamic Collision Avoidance | Björn Lütjens |
| A07 | Evaluating 'Graphical Perception' with CNNs | Daniel Haehn |
| A08 | Individual Neurons in Neural Machine Translation | Yonatan Belinkov |
| A09 | Robustness may be at odds with accuracy | Dimitris Tsipras |
| A10 | Analyzing Gradients to Detect Backdoors in Deep Neural Networks | Ebube Chuba |
| A11 | Towards Functional Transparency: a Game-Theoretic Approach | Guang-He Lee |
| A12 | All you need to train deep residual networks is a good initialization | Hongyi Zhang |
| A13 | ResNet with one-neuron hidden layers is a Universal Approximator | Hongzhou Lin |
| A14 | Quantum optical neural networks | Jacques Carolan |
| A15 | Comparing deep neural network and human representations via sound synthesis | Jenelle Feather |
| A16 | Defensive Quantization: When Efficiency Meets Robustness | Ji Lin |
| A17 | The Lottery Ticket Hypothesis | Jonathan Frankle |
| A18 | Symbolic Relation Networks for Reinforcement Learning | Josh Joseph |
| A19 | Automating Stylistic Bias Detection in Sentiment Analysis | Judy Hanwen Shen |
| A20 | Visual Inspection of Saliency Maps Can Provide a False Sense of Security | Julius Adebayo |
| B21 | Towards Robust, Locally Linear Deep Networks | Guang-He Lee |
| B22 | Learning Symbolic Rules Through Explanation | Leilani H. Gilpin |
| B23 | Efficient Neural Network Robustness Certification with General Activation Functions | Lily Weng |
| B24 | Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability | Mahi Shafiullah |
| B25 | Inverse Graphics with Probabilistic Programming and Deep Learning for Inference | Marco Cusumano-Towner |
| B26 | Examining Learned Class Relationships in Deep Neural Networks | Mathew Monfort |
| B27 | Neural Networks Trained to Estimate F0 from Natural Sounds Replicate Properties of Human Pitch Perception | Ray Gonzalez |
| B28 | Targeted Syntactic Evaluation of LSTMs and Recurrent Neural Network Grammars | Roger Levy |
| B29 | Understanding Phase-Coded Neural Networks and their Scalability | Rumen Dangovski |
| B30 | Verification of Dynamical Systems with Piecewise Affine Machine Learning Elements | Sadra Sadraddini |
| B31 | Minimal Images in Deep Neural Networks: DNN Failures on Natural Images | Sanjana Srivastava |
| B32 | Verification of Recurrent Neural Networks via Systems and Control Theory | Shen Shen |
| B33 | Utility Interpretation in Deep Neural Network | Shenhao Wang |
| B34 | Providing Rationales and Pragmatically Effective Modifications | Sudhanshu Mishra |
| B35 | Detecting Egregious Responses in Neurl Sequence-to-Sequence Models | Tianxing He |
| B36 | Redundancy Emerges in Overparametrized Deep Neural Networks | Xavier Boix |
| B37 | Emergence of topographical correspondences between deep neural network and human brain visual cortex | Yalda Mohsenzadeh |
| B38 | Comparing multi-task and single-task network interpretability | Kandan Ramakrishnan |
| B39 | Visualizing and Understanding Generative Adversarial Networks | David Bau |