Accepted

INFO: Posters in gathertown will follow the numbering below.

• (Poster #01): Ali Borji. Shape Defense

• (Poster #03): Tatiana Shavrina, Valentin Malykh. How not to Lie with a Benchmark: Rearranging NLP Leaderboards

• (Poster #05): Soufiane Hayou, Arnaud Doucet, Judith Rousseau. The Curse of Depth in Kernel Regime

• (Poster #06): Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin. Pathologies in Priors and Inference for Bayesian Transformers

• (Poster #07): Amélie Chatelain, Amine Djeghri, Daniel Hesslow, Julien Launay, Iacopo Poli. Is the Number of Trainable Parameters All That Actually Matters?

• (Poster #08): Iryna Korshunova, Minqi Jiang, Jack Parker-Holder, Tim Rocktäschel, Edward Grefenstette. Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay

• (Poster #09): Dyah Adila, Dongyeop Kang. Understanding Out-of-distribution: A Perspective of Data Dynamics

• (Poster #10): David R. Burt, Artem Artemev, Mark van der Wilk. Barely Biased Learning for Gaussian Process Regression

• (Poster #11): Guoxuan Xia, Sangwon Ha, Tiago Azevedo, Partha Maji. An Underexplored Dilemma between Confidence and Calibration in Quantized Neural Networks

• (Poster #12): Wouter Kool, Chris J. Maddison, Andriy Mnih. Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts

• (Poster #13): David Kappel, Franscesco Negri, Christian Tetzlaff. Continual Learning with Memory Cascades

• (Poster #14): Nitya Kasturi, Igor Markov. Text Ranking and Classification using Data Compression

• (Poster #15): Sandhya Prabhakaran. A multivariate extension to the Exponentially-modified Gaussian distribution

• (Poster #16): Mike Van Ness, Madeleine Udell. CDF Normalization for Controlling the Distribution of Hidden Layer Activations

• (Poster #17): Chelsea Murray, James Urquhart Allingham, Javier Antoran, José Miguel Hernández-Lobato. Addressing Bias in Active Learning with Depth Uncertainty Networks… or Not

• (Poster #18): Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, Hao Wang. Language Models as Recommender Systems: Evaluations and Limitations

• (Poster #19): Cian Eastwood, Ian Mason, Chris Williams. Unit-level surprise in neural networks

• (Poster #20): Ke Alexander Wang, Danielle C. Maddix, Bernie Wang. GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics

• (Poster #21): Tiffany Joyce Vlaar. Examining neural network behavior at the classification boundary

• (Poster #22): Arno Blaas, Xavier Suau, Jason Ramapuram, Nicholas Apostoloff, Luca Zappella. Challenges of Adversarial Image Augmentations

• (Poster #23): Rainer Kelz, Gerhard Widmer. Nonlinear Denoising, Linear Demixing

• (Poster #24): Bhavya Vasudeva, Puneesh Deora, Saumik Bhattacharya, Umapada Pal, Sukalpa Chanda. GradML: A Gradient-based Loss for Deep Metric Learning

• (Poster #25): Paulo Pirozelli, João F. N. B. Cortese. The Beauty Everywhere: How Aesthetic Criteria Contribute to the Development of AI

• (Poster #26): Anthony L. Caterini, Gabriel Loaiza-Ganem. Entropic Issues in Likelihood-Based OOD Detection

• (Poster #27): David Rohde. Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts

• (Poster #28): Sahil Khose, Shruti Praveen Jain, V Manushree A Studious Approach to Semi-Supervised Learning

• (Poster #29): Ezgi Korkmaz Adversarial Training Blocks Generalization in Neural Policies

• (Poster #30): Larissa Triess, David Peter. Semi-Local Convolutions for LiDAR Scan Processing

• (Poster #31): Benjamin Bloem-Reddy. Beauty in Machine Learning: Fluency and Leaps

• (Poster #32): Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E Vogt On the Limitations of Multimodal VAEs

• (Poster #33): Vedant Shah, Gautam Shroff. Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!

• (Poster #35): Benjamin Zhang, Tuhin Sahai, Youssef Marzouk. Sampling via Controlled Stochastic Dynamical Systems