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1 code implementation • 8 Feb 2023 • Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, Pascale Fung

It is, for example, better at deductive than inductive reasoning.

1 code implementation • 19 Dec 2022 • Samuel Cahyawijaya, Holy Lovenia, Alham Fikri Aji, Genta Indra Winata, Bryan Wilie, Rahmad Mahendra, Christian Wibisono, Ade Romadhony, Karissa Vincentio, Fajri Koto, JENNIFER SANTOSO, David Moeljadi, Cahya Wirawan, Frederikus Hudi, Ivan Halim Parmonangan, Ika Alfina, Muhammad Satrio Wicaksono, Ilham Firdausi Putra, Samsul Rahmadani, Yulianti Oenang, Ali Akbar Septiandri, James Jaya, Kaustubh D. Dhole, Arie Ardiyanti Suryani, Rifki Afina Putri, Dan Su, Keith Stevens, Made Nindyatama Nityasya, Muhammad Farid Adilazuarda, Ryan Ignatius, Ryandito Diandaru, Tiezheng Yu, Vito Ghifari, Wenliang Dai, Yan Xu, Dyah Damapuspita, Cuk Tho, Ichwanul Muslim Karo Karo, Tirana Noor Fatyanosa, Ziwei Ji, Pascale Fung, Graham Neubig, Timothy Baldwin, Sebastian Ruder, Herry Sujaini, Sakriani Sakti, Ayu Purwarianti

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources.

Automatic Speech Recognition
Automatic Speech Recognition (ASR)
**+1**

no code implementations • 3 Dec 2022 • Ziwei Ji, Zihan Liu, Nayeon Lee, Tiezheng Yu, Bryan Wilie, Min Zeng, Pascale Fung

Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses.

1 code implementation • 14 Oct 2022 • Wenliang Dai, Zihan Liu, Ziwei Ji, Dan Su, Pascale Fung

Large-scale vision-language pre-trained (VLP) models are prone to hallucinate non-existent visual objects when generating text based on visual information.

no code implementations • 1 Mar 2022 • Ziwei Ji, Yan Xu, I-Tsun Cheng, Samuel Cahyawijaya, Rita Frieske, Etsuko Ishii, Min Zeng, Andrea Madotto, Pascale Fung

In order to offer a customized script tool and inspire professional scriptwriters, we present VScript.

no code implementations • 9 Feb 2022 • Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir

We initiate a formal study of reproducibility in optimization.

no code implementations • 8 Feb 2022 • Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Wenliang Dai, Andrea Madotto, Pascale Fung

This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation.

no code implementations • 31 Jan 2022 • Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp

In this paper, we close this gap by constructing a well-behaved distribution such that the global minimizer of the logistic risk over this distribution only achieves $\Omega(\sqrt{\textrm{OPT}})$ misclassification risk, matching the upper bound in (Frei et al., 2021).

no code implementations • ICLR 2022 • Yuzheng Hu, Ziwei Ji, Matus Telgarsky

We show that the simplest actor-critic method -- a linear softmax policy updated with TD through interaction with a linear MDP, but featuring no explicit regularization or exploration -- does not merely find an optimal policy, but moreover prefers high entropy optimal policies.

no code implementations • 1 Jul 2021 • Ziwei Ji, Nathan Srebro, Matus Telgarsky

We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e. g., the exponential or logistic loss), which maximizes the classification margin on separable data at a rate of $\widetilde{\mathcal{O}}(1/t^2)$.

no code implementations • NeurIPS 2021 • Ziwei Ji, Justin D. Li, Matus Telgarsky

This work studies the behavior of shallow ReLU networks trained with the logistic loss via gradient descent on binary classification data where the underlying data distribution is general, and the (optimal) Bayes risk is not necessarily zero.

no code implementations • ICLR 2021 • Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang

This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one can distill it into a network with nearly identical predictions but low complexity and vastly smaller generalization bounds.

no code implementations • 11 Jan 2021 • Yejin Bang, Etsuko Ishii, Samuel Cahyawijaya, Ziwei Ji, Pascale Fung

Amid the pandemic COVID-19, the world is facing unprecedented infodemic with the proliferation of both fake and real information.

3 code implementations • 8 Dec 2020 • Zihan Liu, Yan Xu, Tiezheng Yu, Wenliang Dai, Ziwei Ji, Samuel Cahyawijaya, Andrea Madotto, Pascale Fung

Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.

1 code implementation • Findings of the Association for Computational Linguistics 2020 • Dan Su, Yan Xu, Wenliang Dai, Ziwei Ji, Tiezheng Yu, Pascale Fung

Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs.

no code implementations • 19 Jun 2020 • Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky

Recent work across many machine learning disciplines has highlighted that standard descent methods, even without explicit regularization, do not merely minimize the training error, but also exhibit an implicit bias.

no code implementations • NeurIPS 2020 • Ziwei Ji, Matus Telgarsky

In this paper, we show that although the minimizers of cross-entropy and related classification losses are off at infinity, network weights learned by gradient flow converge in direction, with an immediate corollary that network predictions, training errors, and the margin distribution also converge.

no code implementations • ICLR 2020 • Ziwei Ji, Matus Telgarsky, Ruicheng Xian

This paper establishes rates of universal approximation for the shallow neural tangent kernel (NTK): network weights are only allowed microscopic changes from random initialization, which entails that activations are mostly unchanged, and the network is nearly equivalent to its linearization.

no code implementations • ICLR 2020 • Ziwei Ji, Matus Telgarsky

Recent theoretical work has guaranteed that overparameterized networks trained by gradient descent achieve arbitrarily low training error, and sometimes even low test error.

no code implementations • 18 Jun 2019 • Bolton Bailey, Ziwei Ji, Matus Telgarsky, Ruicheng Xian

This paper investigates the approximation power of three types of random neural networks: (a) infinite width networks, with weights following an arbitrary distribution; (b) finite width networks obtained by subsampling the preceding infinite width networks; (c) finite width networks obtained by starting with standard Gaussian initialization, and then adding a vanishingly small correction to the weights.

no code implementations • 11 Jun 2019 • Ziwei Ji, Matus Telgarsky

On the other hand, with a properly chosen but aggressive step size schedule, we prove $O(1/t)$ rates for both $\ell_2$ margin maximization and implicit bias, whereas prior work (including all first-order methods for the general hard-margin linear SVM problem) proved $\widetilde{O}(1/\sqrt{t})$ margin rates, or $O(1/t)$ margin rates to a suboptimal margin, with an implied (slower) bias rate.

no code implementations • ICLR 2019 • Ziwei Ji, Matus Telgarsky

This paper establishes risk convergence and asymptotic weight matrix alignment --- a form of implicit regularization --- of gradient flow and gradient descent when applied to deep linear networks on linearly separable data.

no code implementations • 20 Mar 2018 • Ziwei Ji, Matus Telgarsky

Gradient descent, when applied to the task of logistic regression, outputs iterates which are biased to follow a unique ray defined by the data.

no code implementations • 6 Nov 2017 • Ziwei Ji, Ruta Mehta, Matus Telgarsky

Consider the seller's problem of finding optimal prices for her $n$ (divisible) goods when faced with a set of $m$ consumers, given that she can only observe their purchased bundles at posted prices, i. e., revealed preferences.

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