1 code implementation • 25 Sep 2024 • Harsha Vardhan Simhadri, Martin Aumüller, Amir Ingber, Matthijs Douze, George Williams, Magdalen Dobson Manohar, Dmitry Baranchuk, Edo Liberty, Frank Liu, Ben Landrum, Mazin Karjikar, Laxman Dhulipala, Meng Chen, Yue Chen, Rui Ma, Kai Zhang, Yuzheng Cai, Jiayang Shi, Yizhuo Chen, Weiguo Zheng, Zihao Wan, Jie Yin, Ben Huang
The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect the growing complexity and diversity of workloads.
no code implementations • 16 Apr 2024 • Eric Yeats, Cameron Darwin, Eduardo Ortega, Frank Liu, Hai Li
We leverage diffusion models to study the robustness-performance tradeoff of robust classifiers.
no code implementations • 14 Dec 2023 • Frank Liu, Agniva Chowdhury
In various scientific and engineering applications, there is typically an approximate model of the underlying complex system, even though it contains both aleatoric and epistemic uncertainties.
no code implementations • 11 Dec 2023 • Eric Yeats, Cameron Darwin, Frank Liu, Hai Li
Quantification of the number of variables needed to locally explain complex data is often the first step to better understanding it.
no code implementations • 19 Oct 2023 • Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana, Jan Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge.
no code implementations • 24 Aug 2023 • Karthik Somayaji NS, Yu Wang, Malachi Schram, Jan Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li
Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.
1 code implementation • 8 Feb 2023 • Eric Yeats, Frank Liu, Hai Li
Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues.
1 code implementation • 21 Sep 2022 • Eric Yeats, Frank Liu, David Womble, Hai Li
We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the underlying variation profile (e. g., no assumptions on the number or distribution of the individual latent variables to be extracted).
no code implementations • NeurIPS 2021 • Ján Drgoňa, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar
Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems.
no code implementations • 14 Jan 2020 • Inseok Hwang, Jinho Lee, Frank Liu, Minsik Cho
Our intuition is that, the more similarity exists between the unknown data samples and the part of known data that an autoencoder was trained with, the better chances there could be that this autoencoder makes use of its trained knowledge, reconstructing output samples closer to the originals.
no code implementations • 28 May 2019 • Xiaocong Du, Gouranga Charan, Frank Liu, Yu Cao
Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference.
no code implementations • 1 Nov 2017 • Luca M. Aiello, Rossano Schifanella, Miriam Redi, Stacey Svetlichnaya, Frank Liu, Simon Osindero
Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement.