Search Results for author: Zhan Shi

Found 27 papers, 11 papers with code

Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning

no code implementations Findings (EMNLP) 2021 Zhan Shi, Hui Liu, Martin Renqiang Min, Christopher Malon, Li Erran Li, Xiaodan Zhu

Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training.

Image Captioning Retrieval

Boosting Spectral Clustering on Incomplete Data via Kernel Correction and Affinity Learning

1 code implementation 37th Conference on Neural Information Processing Systems (NeurIPS 2023) 2023 Fangchen Yu, Runze Zhao, Zhan Shi, Yiwen Lu, Jicong Fan, Yicheng Zeng, Jianfeng Mao, Wenye Li

Secondly, we develop a series of affinity learning methods that equip the selfexpressive framework with ℓp-norm to construct an intrinsic affinity matrix with an adaptive extension.

Clustering Imputation

Aperture Diffraction for Compact Snapshot Spectral Imaging

1 code implementation ICCV 2023 Tao Lv, Hao Ye, Quan Yuan, Zhan Shi, Yibo Wang, Shuming Wang, Xun Cao

We demonstrate a compact, cost-effective snapshot spectral imaging system named Aperture Diffraction Imaging Spectrometer (ADIS), which consists only of an imaging lens with an ultra-thin orthogonal aperture mask and a mosaic filter sensor, requiring no additional physical footprint compared to common RGB cameras.

Speaker recognition with two-step multi-modal deep cleansing

1 code implementation28 Oct 2022 Ruijie Tao, Kong Aik Lee, Zhan Shi, Haizhou Li

However, noisy samples (i. e., with wrong labels) in the training set induce confusion and cause the network to learn the incorrect representation.

Representation Learning Speaker Recognition +1

Visual Semantic Parsing: From Images to Abstract Meaning Representation

no code implementations26 Oct 2022 Mohamed Ashraf Abdelsalam, Zhan Shi, Federico Fancellu, Kalliopi Basioti, Dhaivat J. Bhatt, Vladimir Pavlovic, Afsaneh Fazly

The success of scene graphs for visual scene understanding has brought attention to the benefits of abstracting a visual input (e. g., image) into a structured representation, where entities (people and objects) are nodes connected by edges specifying their relations.

Scene Understanding Semantic Parsing

Graph-based Active Learning for Semi-supervised Classification of SAR Data

1 code implementation31 Mar 2022 Kevin Miller, John Mauro, Jason Setiadi, Xoaquin Baca, Zhan Shi, Jeff Calder, Andrea L. Bertozzi

We use a Convolutional Neural Network Variational Autoencoder (CNNVAE) to embed SAR data into a feature space, and then construct a similarity graph from the embedded data and apply graph-based semi-supervised learning techniques.

Active Learning graph construction +1

Unsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference

no code implementations8 Sep 2021 Xiaoyu Yang, Xiaodan Zhu, Zhan Shi, Tianda Li

There have been two lines of approaches that can be used to further address the limitation: (1) unsupervised pretraining can leverage knowledge in much larger unstructured text data; (2) structured (often human-curated) knowledge has started to be considered in neural-network-based models for NLI.

Natural Language Inference Sentence +2

Unsupervised Conversation Disentanglement through Co-Training

1 code implementation EMNLP 2021 Hui Liu, Zhan Shi, Xiaodan Zhu

For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier.

Conversation Disentanglement Disentanglement

Enhancing Descriptive Image Captioning with Natural Language Inference

1 code implementation ACL 2021 Zhan Shi, Hui Liu, Xiaodan Zhu

In this paper we propose a novel approach to encourage captioning models to produce more detailed captions using natural language inference, based on the motivation that, among different captions of an image, descriptive captions are more likely to entail less descriptive captions.

Descriptive Image Captioning +1

An infinite-dimensional representation of the Ray-Knight theorems

no code implementations3 Dec 2020 Elie Aïdékon, Yueyun Hu, Zhan Shi

The classical Ray-Knight theorems for Brownian motion determine the law of its local time process either at the first hitting time of a given value a by the local time at the origin, or at the first hitting time of a given position b by Brownian motion.

Probability

Learned Hardware/Software Co-Design of Neural Accelerators

no code implementations5 Oct 2020 Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin

The use of deep learning has grown at an exponential rate, giving rise to numerous specialized hardware and software systems for deep learning.

Bayesian Optimization

Improving Image Captioning with Better Use of Caption

no code implementations ACL 2020 Zhan Shi, Xu Zhou, Xipeng Qiu, Xiaodan Zhu

Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community.

Image Captioning Inductive Bias +2

Improving Image Captioning with Better Use of Captions

1 code implementation21 Jun 2020 Zhan Shi, Xu Zhou, Xipeng Qiu, Xiaodan Zhu

Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community.

Image Captioning Inductive Bias +2

Generalised Lipschitz Regularisation Equals Distributional Robustness

no code implementations11 Feb 2020 Zac Cranko, Zhan Shi, Xinhua Zhang, Richard Nock, Simon Kornblith

The problem of adversarial examples has highlighted the need for a theory of regularisation that is general enough to apply to exotic function classes, such as universal approximators.

Certifying Distributional Robustness using Lipschitz Regularisation

no code implementations25 Sep 2019 Zac Cranko, Zhan Shi, Xinhua Zhang, Simon Kornblith, Richard Nock

Distributional robust risk (DRR) minimisation has arisen as a flexible and effective framework for machine learning.

Learning Execution through Neural Code Fusion

no code implementations ICLR 2020 Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

In this work, we propose a new approach to use GNNs to learn fused representations of general source code and its execution.

Transfer Learning

Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge

no code implementations22 Apr 2019 Yu-Ping Ruan, Xiaodan Zhu, Zhen-Hua Ling, Zhan Shi, Quan Liu, Si Wei

Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning.

Common Sense Reasoning Sentence

Lipschitz Networks and Distributional Robustness

no code implementations4 Sep 2018 Zac Cranko, Simon Kornblith, Zhan Shi, Richard Nock

Robust risk minimisation has several advantages: it has been studied with regards to improving the generalisation properties of models and robustness to adversarial perturbation.

Inductive Two-Layer Modeling with Parametric Bregman Transfer

no code implementations ICML 2018 Vignesh Ganapathiraman, Zhan Shi, Xinhua Zhang, Yao-Liang Yu

Latent prediction models, exemplified by multi-layer networks, employ hidden variables that automate abstract feature discovery.

Test Transductive Learning +1

Monge blunts Bayes: Hardness Results for Adversarial Training

no code implementations8 Jun 2018 Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder

A key feature of our result is that it holds for all proper losses, and for a popular subset of these, the optimisation of this central measure appears to be independent of the loss.

Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction

no code implementations NeurIPS 2017 Zhan Shi, Xinhua Zhang, Yao-Liang Yu

Adversarial machines, where a learner competes against an adversary, have regained much recent interest in machine learning.

DAG-based Long Short-Term Memory for Neural Word Segmentation

no code implementations2 Jul 2017 Xinchi Chen, Zhan Shi, Xipeng Qiu, Xuanjing Huang

In this paper, we propose a new neural model to incorporate the word-level information for Chinese word segmentation.

Chinese Word Segmentation Feature Engineering +2

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