Search Results for author: Ke Sun

Found 79 papers, 23 papers with code

CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models

1 code implementation29 May 2023 Zhongxi Chen, Ke Sun, Xianming Lin, Rongrong Ji

Due to the stochastic sampling process of diffusion, our model is capable of sampling multiple possible predictions from the mask distribution, avoiding the problem of overconfident point estimation.

Denoising object-detection +2

Gloss-Free End-to-End Sign Language Translation

no code implementations22 May 2023 Kezhou Lin, Xiaohan Wang, Linchao Zhu, Ke Sun, Bang Zhang, Yi Yang

In this paper, we tackle the problem of sign language translation (SLT) without gloss annotations.

Sign Language Translation Translation

InterFormer: Real-time Interactive Image Segmentation

1 code implementation6 Apr 2023 You Huang, Hao Yang, Ke Sun, Shengchuan Zhang, Guannan Jiang, Rongrong Ji, Liujuan Cao

Second, the model has to repeatedly process the image, the annotator's current click, and the model's feedback of the annotator's former clicks at each step of interaction, resulting in redundant computations.

Image Segmentation Interactive Segmentation +1

Mathematical Challenges in Deep Learning

no code implementations24 Mar 2023 Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev, Michael R. Metel, Xinlin Li, Ke Sun, Sobhan Hemati, Masoud Asgharian, Linglong Kong, Wulong Liu, Boxing Chen

Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012.

Transformed Distribution Matching for Missing Value Imputation

no code implementations20 Feb 2023 He Zhao, Ke Sun, Amir Dezfouli, Edwin Bonilla

We study the problem of imputing missing values in a dataset, which has important applications in many domains.


$2 \times 2$ Zero-Sum Games with Commitments and Noisy Observations

no code implementations3 Nov 2022 Ke Sun, Samir M. Perlaza, Alain Jean-Marie

In this paper, $2\times2$ zero-sum games are studied under the following assumptions: $(1)$ One of the players (the leader) commits to choose its actions by sampling a given probability measure (strategy); $(2)$ The leader announces its action, which is observed by its opponent (the follower) through a binary channel; and $(3)$ the follower chooses its strategy based on the knowledge of the leader's strategy and the noisy observation of the leader's action.

How Does Value Distribution in Distributional Reinforcement Learning Help Optimization?

no code implementations29 Sep 2022 Ke Sun, Bei Jiang, Linglong Kong

We consider the problem of learning a set of probability distributions from the Bellman dynamics in distributional reinforcement learning~(RL) that learns the whole return distribution compared with only its expectation in classical RL.

Distributional Reinforcement Learning reinforcement-learning +1

Non-linear Embeddings in Hilbert Simplex Geometry

no code implementations22 Mar 2022 Frank Nielsen, Ke Sun

A key technique of machine learning and computer vision is to embed discrete weighted graphs into continuous spaces for further downstream processing.

RTGNN: A Novel Approach to Model Stochastic Traffic Dynamics

no code implementations21 Feb 2022 Ke Sun, Stephen Chaves, Paul Martin, Vijay Kumar

Because it is difficult to develop first principle models of cars driven by humans, there is great potential for using data driven approaches in developing traffic dynamical models.

Decision Making Motion Planning +1

Distributional Reinforcement Learning via Sinkhorn Iterations

no code implementations1 Feb 2022 Ke Sun, Yingnan Zhao, Yi Liu, Wulong Liu, Bei Jiang, Linglong Kong

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the entire distribution of the total return rather than only its expectation.

Atari Games Distributional Reinforcement Learning +2

Fair Wrapping for Black-box Predictions

1 code implementation31 Jan 2022 Alexander Soen, Ibrahim Alabdulmohsin, Sanmi Koyejo, Yishay Mansour, Nyalleng Moorosi, Richard Nock, Ke Sun, Lexing Xie

We introduce a new family of techniques to post-process ("wrap") a black-box classifier in order to reduce its bias.


Contrastive Laplacian Eigenmaps

1 code implementation NeurIPS 2021 Hao Zhu, Ke Sun, Piotr Koniusz

Starting from a GAN-inspired contrastive formulation, we show that the Jensen-Shannon divergence underlying many contrastive graph embedding models fails under disjoint positive and negative distributions, which may naturally emerge during sampling in the contrastive setting.

Contrastive Learning Graph Embedding

Dual Contrastive Learning for General Face Forgery Detection

no code implementations27 Dec 2021 Ke Sun, Taiping Yao, Shen Chen, Shouhong Ding, Jilin L, Rongrong Ji

With various facial manipulation techniques arising, face forgery detection has drawn growing attention due to security concerns.

Contrastive Learning

Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness

no code implementations3 Nov 2021 Ke Sun, Mingjie Li, Zhouchen Lin

Adversarial robustness, which mainly contains sensitivity-based robustness and spatial robustness, plays an integral part in the robust generalization.

Adversarial Robustness

Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization

no code implementations NeurIPS 2021 Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong

Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL.

reinforcement-learning Reinforcement Learning (RL)

High-order Tensor Pooling with Attention for Action Recognition

no code implementations11 Oct 2021 Piotr Koniusz, Lei Wang, Ke Sun

We aim at capturing high-order statistics of feature vectors formed by a neural network, and propose end-to-end second- and higher-order pooling to form a tensor descriptor.

Ranked #2 on Scene Recognition on YUP++ (using extra training data)

Action Recognition Scene Recognition +1

Interpreting Distributional Reinforcement Learning: A Regularization Perspective

no code implementations7 Oct 2021 Ke Sun, Yingnan Zhao, Yi Liu, Enze Shi, Yafei Wang, Xiaodong Yan, Bei Jiang, Linglong Kong

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation.

Atari Games Distributional Reinforcement Learning +2

Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm

no code implementations29 Sep 2021 Ke Sun, Yingnan Zhao, Yi Liu, Enze Shi, Yafei Wang, Aref Sadeghi, Xiaodong Yan, Bei Jiang, Linglong Kong

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation.

Atari Games Distributional Reinforcement Learning +2

Gaussian Differential Privacy Transformation: from identification to application

no code implementations29 Sep 2021 Yi Liu, Ke Sun, Bei Jiang, Linglong Kong

Gaussian differential privacy (GDP) is a single-parameter family of privacy notions that provides coherent guarantees to avoid the exposure of individuals from machine learning models.

Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations

no code implementations29 Sep 2021 Ke Sun, Yi Liu, Yingnan Zhao, Hengshuai Yao, Shangling Jui, Linglong Kong

In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training.

Distributional Reinforcement Learning reinforcement-learning +1

A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning

no code implementations21 Sep 2021 Hongming Zhang, Ke Sun, Bo Xu, Linglong Kong, Martin Müller

In this paper, we propose a simple yet effective anomaly detection framework for deep RL algorithms that simultaneously considers random, adversarial and out-of-distribution~(OOD) state outliers.

Anomaly Detection Atari Games +3

Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations

no code implementations17 Sep 2021 Ke Sun, Yi Liu, Yingnan Zhao, Hengshuai Yao, Shangling Jui, Linglong Kong

In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training.

Density Estimation Distributional Reinforcement Learning +2

On the Variance of the Fisher Information for Deep Learning

no code implementations NeurIPS 2021 Alexander Soen, Ke Sun

In the realm of deep learning, the Fisher information matrix (FIM) gives novel insights and useful tools to characterize the loss landscape, perform second-order optimization, and build geometric learning theories.

Learning Theory

Secure Quantized Training for Deep Learning

3 code implementations NeurIPS 2021 Marcel Keller, Ke Sun

We implement training of neural networks in secure multi-party computation (MPC) using quantization commonly used in said setting.


Intent Disentanglement and Feature Self-supervision for Novel Recommendation

no code implementations28 Jun 2021 Tieyun Qian, Yile Liang, Qing Li, Xuan Ma, Ke Sun, Zhiyong Peng

Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems.

Disentanglement Recommendation Systems +1

Graph Learning: A Survey

no code implementations3 May 2021 Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, Huan Liu

In this survey, we present a comprehensive overview on the state-of-the-art of graph learning.

BIG-bench Machine Learning Combinatorial Optimization +3

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression

2 code implementations CVPR 2021 Zigang Geng, Ke Sun, Bin Xiao, Zhaoxiang Zhang, Jingdong Wang

Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions.

Keypoint Detection regression

Graph Force Learning

no code implementations7 Mar 2021 Ke Sun, Jiaying Liu, Shuo Yu, Bo Xu, Feng Xia

Features representation leverages the great power in network analysis tasks.

Graph Learning

Network Representation Learning: From Traditional Feature Learning to Deep Learning

no code implementations7 Mar 2021 Ke Sun, Lei Wang, Bo Xu, Wenhong Zhao, Shyh Wei Teng, Feng Xia

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data.

Recommendation Systems Representation Learning

Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting

no code implementations24 Feb 2021 Shunxin Xu, Ke Sun, Dong Liu, Zhiwei Xiong, Zheng-Jun Zha

We observe that not only denoising helps combat the drop of segmentation accuracy due to noise, but also pixel-wise semantic information boosts the capability of denoising.

Image Denoising Semantic Segmentation

Effectiveness of MPC-friendly Softmax Replacement

3 code implementations23 Nov 2020 Marcel Keller, Ke Sun

Softmax is widely used in deep learning to map some representation to a probability distribution.

A Practical Chinese Dependency Parser Based on A Large-scale Dataset

2 code implementations2 Sep 2020 Shuai Zhang, Lijie Wang, Ke Sun, Xinyan Xiao

DDParser is extended on the graph-based biaffine parser to accommodate to the characteristics of Chinese dataset.

Dependency Parsing

Multivariate Relations Aggregation Learning in Social Networks

no code implementations9 Aug 2020 Jin Xu, Shuo Yu, Ke Sun, Jing Ren, Ivan Lee, Shirui Pan, Feng Xia

Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important.

Graph Learning Node Classification

Feedback Enhanced Motion Planning for Autonomous Vehicles

1 code implementation11 Jul 2020 Ke Sun, Brent Schlotfeldt, Stephen Chaves, Paul Martin, Gulshan Mandhyan, Vijay Kumar

In this work, we address the motion planning problem for autonomous vehicles through a new lattice planning approach, called Feedback Enhanced Lattice Planner (FELP).


Classify and Generate Reciprocally: Simultaneous Positive-Unlabelled Learning and Conditional Generation with Extra Data

no code implementations14 Jun 2020 Bing Yu, Ke Sun, He Wang, Zhouchen Lin, Zhanxing Zhu

In particular, we present a novel training framework to jointly target both PU classification and conditional generation when exposing to extra data, especially out-of-distribution unlabeled data, by exploring the interplay between them: 1) enhancing the performance of PU classifiers with the assistance of a novel Conditional Generative Adversarial Network~(CGAN) that is robust to noisy labels, 2) leveraging extra data with predicted labels from a PU classifier to help the generation.

Classification General Classification

Seq2seq Translation Model for Sequential Recommendation

no code implementations16 Dec 2019 Ke Sun, Tieyun Qian

We then generalize recent advancements in translation model from sequences of words in two languages to sequences of items and contexts in recommender systems.

Sequential Recommendation Translation

Information-Geometric Set Embeddings (IGSE): From Sets to Probability Distributions

no code implementations27 Nov 2019 Ke Sun, Frank Nielsen

This letter introduces an abstract learning problem called the "set embedding": The objective is to map sets into probability distributions so as to lose less information.

Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy

no code implementations21 Nov 2019 Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu

Furthermore, by explicitly constructing a patch-level graph in the different network layers and interpolating the neighborhood features to refine the representation of the current sample, our Patch-level Neighborhood Interpolation can then be applied to enhance two popular regularization strategies, namely Virtual Adversarial Training (VAT) and MixUp, yielding their neighborhood versions.

A Note on Our Submission to Track 4 of iDASH 2019

no code implementations24 Oct 2019 Marcel Keller, Ke Sun

iDASH is a competition soliciting implementations of cryptographic schemes of interest in the context of biology.

BIG-bench Machine Learning

Deep High-Resolution Representation Learning for Visual Recognition

35 code implementations20 Aug 2019 Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, Wenyu Liu, Bin Xiao

High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection.

 Ranked #1 on Object Detection on COCO test-dev (Hardware Burden metric)

Dichotomous Image Segmentation Face Alignment +7

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models

1 code implementation ICLR 2021 Ke Sun, Zhanxing Zhu, Zhouchen Lin

The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way.

Node Classification

Learning Deep Image Priors for Blind Image Denoising

no code implementations4 Jun 2019 Xianxu Hou, Hongming Luo, Jingxin Liu, Bolei Xu, Ke Sun, Yuanhao Gong, Bozhi Liu, Guoping Qiu

In this paper, we propose an effective image denoising method by learning two image priors from the perspective of domain alignment.

Image Denoising SSIM

Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training

no code implementations4 Jun 2019 Xianxu Hou, Ke Sun, Linlin Shen, Guoping Qiu

We present a new method for improving the performances of variational autoencoder (VAE).

A Geometric Modeling of Occam's Razor in Deep Learning

no code implementations27 May 2019 Ke Sun, Frank Nielsen

Why do deep neural networks (DNNs) benefit from very high dimensional parameter spaces?

Fisher-Bures Adversary Graph Convolutional Networks

1 code implementation11 Mar 2019 Ke Sun, Piotr Koniusz, Zhen Wang

We try to minimize the loss wrt the perturbed $G+\Delta{G}$ while making $\Delta{G}$ to be effective in terms of the Fisher information of the neural network.

Node Classification

Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors

no code implementations28 Feb 2019 Ke Sun, Zhanxing Zhu, Zhouchen Lin

In this paper, we present a systematic study on adversarial examples from three aspects: the amount of training data, task-dependent and model-specific factors.

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels

1 code implementation28 Feb 2019 Ke Sun, Zhouchen Lin, Zhanxing Zhu

In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised(M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes.

Graph Embedding Graph Learning +1

Virtual Adversarial Training on Graph Convolutional Networks in Node Classification

no code implementations28 Feb 2019 Ke Sun, Zhouchen Lin, Hantao Guo, Zhanxing Zhu

The effectiveness of Graph Convolutional Networks (GCNs) has been demonstrated in a wide range of graph-based machine learning tasks.

Classification General Classification +1

Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN

no code implementations28 Feb 2019 Ke Sun, Zhanxing Zhu, Zhouchen Lin

In this work, we propose a novel defense mechanism called Boundary Conditional GAN to enhance the robustness of deep neural networks against adversarial examples.

Data Augmentation

Deep High-Resolution Representation Learning for Human Pose Estimation

38 code implementations CVPR 2019 Ke Sun, Bin Xiao, Dong Liu, Jingdong Wang

We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel.

2D Human Pose Estimation Instance Segmentation +6

On The Chain Rule Optimal Transport Distance

no code implementations19 Dec 2018 Frank Nielsen, Ke Sun

We experimentally evaluate our new family of distances by quantifying the upper bounds of several jointly convex distances between statistical mixtures, and by proposing a novel efficient method to learn Gaussian mixture models (GMMs) by simplifying kernel density estimators with respect to our distance.

Representation Learning of Compositional Data

2 code implementations NeurIPS 2018 Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun

Our approach combines the benefits of the log-ratio transformation from compositional data analysis and exponential family PCA.

Representation Learning

Intrinsic Universal Measurements of Non-linear Embeddings

no code implementations5 Nov 2018 Ke Sun

We try to make a formal definition on the amount of information imposed by a non-linear mapping $f$.

BIG-bench Machine Learning

Stochastic 2-D Motion Planning with a POMDP Framework

no code implementations29 Sep 2018 Ke Sun, Vijay Kumar

Motion planning is challenging when it comes to the case of imperfect state information.


Chinese Lexical Analysis with Deep Bi-GRU-CRF Network

2 code implementations5 Jul 2018 Zhenyu Jiao, Shuqi Sun, Ke Sun

Lexical analysis is believed to be a crucial step towards natural language understanding and has been widely studied.

Lexical Analysis named-entity-recognition +4

Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures

no code implementations29 Jun 2018 Frank Nielsen, Ke Sun

The total variation distance is a core statistical distance between probability measures that satisfies the metric axioms, with value always falling in $[0, 1]$.

Two-sample testing

q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators

1 code implementation1 Jun 2018 Frank Nielsen, Ke Sun

We propose a new generic type of stochastic neurons, called $q$-neurons, that considers activation functions based on Jackson's $q$-derivatives with stochastic parameters $q$.

Frank-Wolfe Network: An Interpretable Deep Structure for Non-Sparse Coding

1 code implementation28 Feb 2018 Dong Liu, Ke Sun, Zhangyang Wang, Runsheng Liu, Zheng-Jun Zha

We propose an interpretable deep structure namely Frank-Wolfe Network (F-W Net), whose architecture is inspired by unrolling and truncating the Frank-Wolfe algorithm for solving an $L_p$-norm constrained problem with $p\geq 1$.

Handwritten Digit Recognition Image Denoising +2

Dense 3-D Mapping with Spatial Correlation via Gaussian Filtering

no code implementations23 Jan 2018 Ke Sun, Kelsey Saulnier, Nikolay Atanasov, George J. Pappas, Vijay Kumar

Many accurate and efficient methods exist that address this problem but most assume that the occupancy states of different elements in the map representation are statistically independent.


Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

no code implementations6 Dec 2017 Kartik Mohta, Michael Watterson, Yash Mulgaonkar, Sikang Liu, Chao Qu, Anurag Makineni, Kelsey Saulnier, Ke Sun, Alex Zhu, Jeffrey Delmerico, Konstantinos Karydis, Nikolay Atanasov, Giuseppe Loianno, Davide Scaramuzza, Kostas Daniilidis, Camillo Jose Taylor, Vijay Kumar

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment.


Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight

11 code implementations30 Nov 2017 Ke Sun, Kartik Mohta, Bernd Pfrommer, Michael Watterson, Sikang Liu, Yash Mulgaonkar, Camillo J. Taylor, Vijay Kumar

However, we still encounter challenges in terms of improving the computational efficiency and robustness of the underlying algorithms for applications in autonomous flight with micro aerial vehicles in which it is difficult to use high quality sensors and pow- erful processors because of constraints on size and weight.


Human Pose Estimation using Global and Local Normalization

no code implementations ICCV 2017 Ke Sun, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Dong Liu, Jingdong Wang

We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation.

Pose Estimation

Relative Fisher Information and Natural Gradient for Learning Large Modular Models

no code implementations ICML 2017 Ke Sun, Frank Nielsen

Fisher information and natural gradient provided deep insights and powerful tools to artificial neural networks.

Clustering in Hilbert simplex geometry

no code implementations3 Apr 2017 Frank Nielsen, Ke Sun

In the Hilbert simplex geometry, the distance is the non-separable Hilbert's metric distance which satisfies the property of information monotonicity with distance level set functions described by polytope boundaries.

Coarse Grained Exponential Variational Autoencoders

no code implementations25 Feb 2017 Ke Sun, Xiangliang Zhang

Variational autoencoders (VAE) often use Gaussian or category distribution to model the inference process.

On Hölder projective divergences

no code implementations14 Jan 2017 Frank Nielsen, Ke Sun, Stéphane Marchand-Maillet

We describe a framework to build distances by measuring the tightness of inequalities, and introduce the notion of proper statistical divergences and improper pseudo-divergences.

Deep Feature Consistent Variational Autoencoder

13 code implementations2 Oct 2016 Xianxu Hou, Linlin Shen, Ke Sun, Guoping Qiu

We present a novel method for constructing Variational Autoencoder (VAE).

Style Transfer

Automatic Visual Theme Discovery from Joint Image and Text Corpora

no code implementations7 Sep 2016 Ke Sun, Xianxu Hou, Qian Zhang, Guoping Qiu

Furthermore, not all tags have the same descriptive power for visual contents and large vocabulary available from natural language could result in a very diverse set of keywords.

Image Retrieval Semantic Similarity +2

Object Specific Deep Learning Feature and Its Application to Face Detection

no code implementations6 Sep 2016 Xianxu Hou, Ke Sun, Linlin Shen, Guoping Qiu

We present a method for discovering and exploiting object specific deep learning features and use face detection as a case study.

Face Detection

Relative Natural Gradient for Learning Large Complex Models

no code implementations20 Jun 2016 Ke Sun, Frank Nielsen

Fisher information and natural gradient provided deep insights and powerful tools to artificial neural networks.

Guaranteed bounds on the Kullback-Leibler divergence of univariate mixtures using piecewise log-sum-exp inequalities

no code implementations19 Jun 2016 Frank Nielsen, Ke Sun

Information-theoretic measures such as the entropy, cross-entropy and the Kullback-Leibler divergence between two mixture models is a core primitive in many signal processing tasks.

Space-Time Local Embeddings

no code implementations NeurIPS 2015 Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet

We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space.

Dimensionality Reduction

Two-Stage Metric Learning

no code implementations12 May 2014 Jun Wang, Ke Sun, Fei Sha, Stephane Marchand-Maillet, Alexandros Kalousis

This induces in the input data space a new family of distance metric with unique properties.

Metric Learning Vocal Bursts Valence Prediction

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