no code implementations • 26 Mar 2024 • Jae-hee So, Joonhwan Chang, Eunji Kim, Junho Na, JiYeon Choi, Jy-yong Sohn, Byung-Hoon Kim, Sang Hui Chu
Recent advancements in Large Language Models (LLMs) have accelerated their usage in various domains.
no code implementations • 21 Mar 2024 • Sehee Lim, Yejin Kim, Chi-Hyun Choi, Jy-yong Sohn, Byung-Hoon Kim
Improving the accessibility of psychotherapy with the aid of Large Language Models (LLMs) is garnering a significant attention in recent years.
no code implementations • 27 Feb 2024 • Juyeon Kim, Jeongeun Lee, Yoonho Chang, Chanyeol Choi, JunSeong Kim, Jy-yong Sohn
Mitigating hallucination issues is one of the main challenges of LLMs we need to overcome, in order to reliably use them in real-world scenarios.
no code implementations • 20 Feb 2024 • Chungpa Lee, Joonhwan Chang, Jy-yong Sohn
The optimal embedding ranges from simplex equiangular-tight-frame to antipodal structure, depending on the temperature parameter used in the sigmoid loss.
no code implementations • 16 Feb 2024 • Yoonjeong Park, HyunJin Kim, Chanyeol Choi, JunSeong Kim, Jy-yong Sohn
The basic idea of CoT is to let LLMs break down their thought processes step-by-step by putting exemplars in the input prompt.
1 code implementation • 12 Jul 2023 • Jaewoong Cho, Kartik Sreenivasan, Keon Lee, Kyunghoo Mun, Soheun Yi, Jeong-Gwan Lee, Anna Lee, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee
Contrastive learning has gained significant attention as a method for self-supervised learning.
1 code implementation • 30 Jan 2023 • Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos
We present a framework for using transformer networks as universal computers by programming them with specific weights and placing them in a loop.
no code implementations • 16 Dec 2022 • Sangyeop Yeo, Yoojin Jang, Jy-yong Sohn, Dongyoon Han, Jaejun Yoo
To the best of our knowledge, we are the first to show the existence of strong lottery tickets in generative models and provide an algorithm to find it stably.
1 code implementation • 13 Oct 2022 • Ozgur Guldogan, Yuchen Zeng, Jy-yong Sohn, Ramtin Pedarsani, Kangwook Lee
In order to promote long-term fairness, we propose a new fairness notion called Equal Improvability (EI), which equalizes the potential acceptance rate of the rejected samples across different groups assuming a bounded level of effort will be spent by each rejected sample.
1 code implementation • 14 Jun 2022 • Tuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee
LIFT does not make any changes to the model architecture or loss function, and it solely relies on the natural language interface, enabling "no-code machine learning with LMs."
1 code implementation • 23 May 2022 • Tuan Dinh, Jy-yong Sohn, Shashank Rajput, Timothy Ossowski, Yifei Ming, Junjie Hu, Dimitris Papailiopoulos, Kangwook Lee
Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods.
no code implementations • 12 Apr 2022 • Changhun Jo, Jy-yong Sohn, Kangwook Lee
Minimizing risk with fairness constraints is one of the popular approaches to learning a fair classifier.
1 code implementation • 24 Feb 2022 • Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric Xing, Kangwook Lee, Dimitris Papailiopoulos
Frankle & Carbin conjecture that we can avoid this by training "lottery tickets", i. e., special sparse subnetworks found at initialization, that can be trained to high accuracy.
no code implementations • 7 Jan 2022 • Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, Kangwook Lee
Mixup is a data augmentation method that generates new data points by mixing a pair of input data.
no code implementations • 18 Oct 2021 • Kartik Sreenivasan, Shashank Rajput, Jy-yong Sohn, Dimitris Papailiopoulos
A recent work by Ramanujan et al. (2020) provides significant empirical evidence that sufficiently overparameterized, random neural networks contain untrained subnetworks that achieve state-of-the-art accuracy on several predictive tasks.
no code implementations • 10 Dec 2020 • Beongjun Choi, Jy-yong Sohn, Dong-Jun Han, Jaekyun Moon
Through extensive real-world experiments, we demonstrate that our scheme, using only $20 \sim 30\%$ of the resources required in the conventional scheme, maintains virtually the same levels of reliability and data privacy in practical federated learning systems.
2 code implementations • NeurIPS 2020 • Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris Papailiopoulos
Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training.
no code implementations • NeurIPS 2020 • Jy-yong Sohn, Dong-Jun Han, Beongjun Choi, Jaekyun Moon
Recent advances in large-scale distributed learning algorithms have enabled communication-efficient training via SignSGD.