no code implementations • EACL (AdaptNLP) 2021 • Jezabel Garcia, Federica Freddi, Jamie McGowan, Tim Nieradzik, Feng-Ting Liao, Ye Tian, Da-Shan Shiu, Alberto Bernacchia
In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related.
Cross-Lingual Natural Language Inference Cross-Lingual Transfer +1
no code implementations • SIGDIAL (ACL) 2021 • Ye Tian, Tim Nieradzik, Sepehr Jalali, Da-Shan Shiu
Analysis on sentence embeddings of disfluent and fluent sentence pairs reveals that the deeper the layer, the more similar their representation (exp2).
no code implementations • 5 Mar 2024 • Chan-Jan Hsu, Chang-Le Liu, Feng-Ting Liao, Po-chun Hsu, Yi-Chang Chen, Da-Shan Shiu
Breeze-7B is an open-source language model based on Mistral-7B, designed to address the need for improved language comprehension and chatbot-oriented capabilities in Traditional Chinese.
1 code implementation • 15 Sep 2023 • Chan-Jan Hsu, Chang-Le Liu, Feng-Ting Liao, Po-chun Hsu, Yi-Chang Chen, Da-Shan Shiu
In an effort to advance the evaluation of language models in Traditional Chinese and stimulate further research in this field, we have open-sourced our benchmark and opened the model for trial.
no code implementations • 10 Aug 2023 • Ushnish Sengupta, Chinkuo Jao, Alberto Bernacchia, Sattar Vakili, Da-Shan Shiu
In this paper, we propose a diffusion model based channel sampling approach for rapidly synthesizing channel realizations from limited data.
1 code implementation • 18 Jul 2023 • Feng-Ting Liao, Yung-Chieh Chan, Yi-Chang Chen, Chan-Jan Hsu, Da-Shan Shiu
In this work, we propose a method to create domain-sensitive speech recognition models that utilize textual domain information by conditioning its generation on a given text prompt.
1 code implementation • 1 Jun 2023 • Ayan Das, Stathi Fotiadis, Anil Batra, Farhang Nabiei, FengTing Liao, Sattar Vakili, Da-Shan Shiu, Alberto Bernacchia
We compute the shortest path according to this metric, and we show that it corresponds to a combination of image sharpening, rather than blurring, and noise deblurring.
1 code implementation • 8 Mar 2023 • Philipp Ennen, Po-chun Hsu, Chan-Jan Hsu, Chang-Le Liu, Yen-chen Wu, Yin-Hsiang Liao, Chin-Tung Lin, Da-Shan Shiu, Wei-Yun Ma
In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese.
no code implementations • 13 Apr 2022 • Fu-Chieh Chang, Yu-Wei Tseng, Ya-Wen Yu, Ssu-Rui Lee, Alexandru Cioba, I-Lun Tseng, Da-Shan Shiu, Jhih-Wei Hsu, Cheng-Yuan Wang, Chien-Yi Yang, Ren-Chu Wang, Yao-Wen Chang, Tai-Chen Chen, Tung-Chieh Chen
Recently, successful applications of reinforcement learning to chip placement have emerged.
no code implementations • 8 Feb 2022 • Sattar Vakili, Jonathan Scarlett, Da-Shan Shiu, Alberto Bernacchia
Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization.
no code implementations • 13 Sep 2021 • Sattar Vakili, Michael Bromberg, Jezabel Garcia, Da-Shan Shiu, Alberto Bernacchia
As a byproduct of our results, we show the equivalence between the RKHS corresponding to the NT kernel and its counterpart corresponding to the Mat\'ern family of kernels, showing the NT kernels induce a very general class of models.
no code implementations • NeurIPS 2021 • Sattar Vakili, Nacime Bouziani, Sepehr Jalali, Alberto Bernacchia, Da-Shan Shiu
Consider the sequential optimization of a continuous, possibly non-convex, and expensive to evaluate objective function $f$.
no code implementations • 21 May 2021 • Philipp Ennen, Yen-Ting Lin, Ali Girayhan Ozbay, Ferdinando Insalata, Maolin Li, Ye Tian, Sepehr Jalali, Da-Shan Shiu
In light of the recent success of data-driven approaches, we propose the novel future bridging NLG (FBNLG) concept for dialogue systems and simulators.
no code implementations • 15 Mar 2021 • Alexandru Cioba, Michael Bromberg, Qian Wang, Ritwik Niyogi, Georgios Batzolis, Jezabel Garcia, Da-Shan Shiu, Alberto Bernacchia
We show that: 1) If tasks are homogeneous, there is a uniform optimal allocation, whereby all tasks get the same amount of data; 2) At fixed budget, there is a trade-off between number of tasks and number of data points per task, with a unique solution for the optimum; 3) When trained separately, harder task should get more data, at the cost of a smaller number of tasks; 4) When training on a mixture of easy and hard tasks, more data should be allocated to easy tasks.
no code implementations • 8 Mar 2021 • Jezabel R. Garcia, Federica Freddi, Feng-Ting Liao, Jamie McGowan, Tim Nieradzik, Da-Shan Shiu, Ye Tian, Alberto Bernacchia
We show that TreeMAML improves the state of the art results for cross-lingual Natural Language Inference.
Cross-Lingual Natural Language Inference Cross-Lingual Transfer +2
no code implementations • 1 Jan 2021 • Jezabel Garcia, Federica Freddi, Jamie McGowan, Tim Nieradzik, Da-Shan Shiu, Ye Tian, Alberto Bernacchia
In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related, and sharing information between unrelated tasks might hurt performance.
no code implementations • 1 Jan 2021 • Georgios Batzolis, Alberto Bernacchia, Da-Shan Shiu, Michael Bromberg, Alexandru Cioba
They are tested on benchmarks with a fixed number of data-points for each training task, and this number is usually arbitrary, for example, 5 instances per class in few-shot classification.
no code implementations • NeurIPS Workshop SVRHM 2021 • Federica Freddi, Jezabel R Garcia, Michael Bromberg, Sepehr Jalali, Da-Shan Shiu, Alvin Chua, Alberto Bernacchia
We propose a novel architecture that allows flexible information flow between features $z$ and locations $(x, y)$ across the entire image with a small number of layers.