1 code implementation • EMNLP 2020 • Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
no code implementations • Findings (ACL) 2022 • YUREN MAO, Zekai Wang, Weiwei Liu, Xuemin Lin, Pengtao Xie
Task weighting, which assigns weights on the including tasks during training, significantly matters the performance of Multi-task Learning (MTL); thus, recently, there has been an explosive interest in it.
no code implementations • 18 Nov 2023 • Duy Minh Ho Nguyen, Tan Ngoc Pham, Nghiem Tuong Diep, Nghi Quoc Phan, Quang Pham, Vinh Tong, Binh T. Nguyen, Ngan Hoang Le, Nhat Ho, Pengtao Xie, Daniel Sonntag, Mathias Niepert
Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging.
1 code implementation • 20 Jun 2023 • Duy M. H. Nguyen, Hoang Nguyen, Nghiem T. Diep, Tan N. Pham, Tri Cao, Binh T. Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag, Mathias Niepert
While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on medical tasks is limited due to the significant domain shift between natural and medical images.
1 code implementation • 18 May 2023 • Youwei Liang, Ruiyi Zhang, Li Zhang, Pengtao Xie
The DrugChat system consists of a graph neural network (GNN), a large language model (LLM), and an adaptor.
no code implementations • 2 Apr 2023 • Ramtin Hosseini, Li Zhang, Bhanu Garg, Pengtao Xie
Our proposed framework involves three stages of learning, which are formulated as a three-level optimization problem: (i) learning to group problems into different subgroups; (ii) learning group-specific sub-models for problem-solving; and (iii) updating group assignments of training examples by minimizing the validation loss.
no code implementations • 18 Feb 2023 • Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun
This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
no code implementations • 9 Jan 2023 • Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh Leather, Yuandong Tian
Finding the optimal pass sequence of compilation can lead to a significant reduction in program size and/or improvement in program efficiency.
no code implementations • 30 Dec 2022 • Hasan Md Tusfiqur, Duy M. H. Nguyen, Mai T. N. Truong, Triet A. Nguyen, Binh T. Nguyen, Michael Barz, Hans-Juergen Profitlich, Ngoc T. T. Than, Ngan Le, Pengtao Xie, Daniel Sonntag
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment.
no code implementations • 4 Dec 2022 • Duy M. H. Nguyen, Hoang Nguyen, Mai T. N. Truong, Tri Cao, Binh T. Nguyen, Nhat Ho, Paul Swoboda, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag
Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data.
no code implementations • 15 Nov 2022 • Qian Li, JianXin Li, Lihong Wang, Cheng Ji, Yiming Hei, Jiawei Sheng, Qingyun Sun, Shan Xue, Pengtao Xie
To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED, leveraging a semantic channel and a topological channel to enrich information interaction from short texts.
1 code implementation • NIPS 2022 • Ramtin Hosseini, Pengtao Xie
At the second stage, saliency maps are generated using the trained model.
1 code implementation • 5 Jul 2022 • Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric Xing
Gradient-based multilevel optimization (MLO) has gained attention as a framework for studying numerous problems, ranging from hyperparameter optimization and meta-learning to neural architecture search and reinforcement learning.
1 code implementation • 16 Feb 2022 • Youwei Liang, Chongjian Ge, Zhan Tong, Yibing Song, Jue Wang, Pengtao Xie
Second, by maintaining the same computational cost, our method empowers ViTs to take more image tokens as input for recognition accuracy improvement, where the image tokens are from higher resolution images.
no code implementations • 4 Jan 2022 • Wenwu Zhu, Xin Wang, Pengtao Xie
Inspired by the concept of self-directed human learning, we introduce the principal concept of Self-directed Machine Learning (SDML) and propose a framework for SDML.
no code implementations • CVPR 2022 • Pengtao Xie, Xuefeng Du
In existing MKD methods, mutual knowledge distillation is performed between models without scrutiny: a worse-performing model is allowed to generate knowledge to train a better-performing model, which may lead to collective failures.
no code implementations • 1 Dec 2021 • Jay Gala, Pengtao Xie
In this work, we aim to investigate how effectively we can leverage this exceptional learning ability to improve machine learning models.
no code implementations • 30 Nov 2021 • Ruisi Zhang, Youwei Liang, Sai Ashish Somayajula, Pengtao Xie
We introduce a training strategy called ``Differentiable Architecture Search with a Generative Model(DASGM)."
1 code implementation • 11 Nov 2021 • Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric Xing, Pengtao Xie
We propose a novel machine learning method called Learning From Mistakes (LFM), wherein the learner improves its ability to learn by focusing more on the mistakes during revision.
1 code implementation • ICLR 2022 • Youwei Liang, Chongjian Ge, Zhan Tong, Yibing Song, Jue Wang, Pengtao Xie
Second, by maintaining the same computational cost, our method empowers ViTs to take more image tokens as input for recognition accuracy improvement, where the image tokens are from higher resolution images.
no code implementations • 22 Sep 2021 • Shentong Mo, Pengtao Xie
Learning by examples, which learns to solve a new problem by looking into how similar problems are solved, is an effective learning method in human learning.
1 code implementation • ACL 2021 • Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
Training complex dialog generation models on small datasets bears high risk of overfitting.
1 code implementation • ACL 2021 • Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie
In this paper, we aim to develop a pathological visual question answering framework to analyze pathology images and answer medical questions related to these images.
no code implementations • 12 Mar 2021 • Hao Ban, Pengtao Xie
Inspired by the interleaving learning technique of humans, in this paper we explore whether this learning methodology is beneficial for improving the performance of machine learning models as well.
no code implementations • 11 Mar 2021 • Parth Sheth, Yueyu Jiang, Pengtao Xie
In the LBT framework, a teacher model improves itself by teaching a student model to learn well.
1 code implementation • 9 Mar 2021 • Meng Zhou, Zechen Li, Pengtao Xie
The SSL task is unsupervised, which is defined purely on input texts without using any human-provided labels.
no code implementations • 28 Dec 2020 • Xingchen Zhao, Xuehai He, Pengtao Xie
We propose a novel machine learning framework referred to as learning by ignoring (LBI).
no code implementations • 23 Dec 2020 • Ramtin Hosseini, Pengtao Xie
In our approach, an explainer model improves its learning ability by trying to clearly explain to an audience model regarding how a prediction outcome is made.
1 code implementation • 23 Dec 2020 • Xuefeng Du, Pengtao Xie
SGL is formulated as a multi-level optimization framework consisting of three learning stages: each learner trains a model independently and uses this model to perform pseudo-labeling; each learner trains another model using datasets pseudo-labeled by other learners; learners improve their architectures by minimizing validation losses.
no code implementations • CVPR 2021 • Ramtin Hosseini, Xingyi Yang, Pengtao Xie
To address this problem, we propose methods to perform differentiable search of robust neural architectures.
no code implementations • 9 Dec 2020 • Pengtao Xie, Xuefeng Du, Hao Ban
To achieve this goal, we develop a general framework -- Skillearn, which provides a principled way to represent humans' learning skills mathematically and use the formally-represented skills to improve the training of ML models.
no code implementations • 30 Nov 2020 • Xuefeng Du, Haochen Zhang, Pengtao Xie
We propose a multi-level optimization framework to formulate LPT, where the tester learns to create difficult and meaningful tests and the learner learns to pass these tests.
no code implementations • 30 Oct 2020 • Hongbo Zou, Guangjing Chen, Pengtao Xie, Sean Chen, Yongtian He, Hochih Huang, Zheng Nie, Hongbao Zhang, Tristan Bala, Kazi Tulip, Yuqi Wang, Shenlin Qin, Eric P. Xing
However, manufacturers and solution partners need to understand how to implement and integrate an AI model into the existing industrial control system.
no code implementations • 7 Oct 2020 • Yue Yang, Pengtao Xie
While deep learning methods have shown great success in medical image analysis, they require a number of medical images to train.
no code implementations • 6 Oct 2020 • Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie
To deal with the issue that a publicly available pathology VQA dataset is lacking, we create PathVQA dataset.
no code implementations • 16 Sep 2020 • Ruisi Zhang, Luntian Mou, Pengtao Xie
Based on these two ideas, we propose a TreeGAN model which consists of three modules: (1) a class hierarchy encoder (CHE) which takes the hierarchical structure of classes and their textual names as inputs and learns an embedding for each class; the embedding captures the hierarchical relationship among classes; (2) a conditional image generator (CIG) which takes the CHE-generated embedding of a class as input and generates a set of images belonging to this class; (3) a consistency checker which performs hierarchical classification on the generated images and checks whether the generated images are compatible with the class hierarchy; the consistency score is used to guide the CIG to generate hierarchy-compatible images.
no code implementations • 13 Sep 2020 • Jiaqi Zeng, Pengtao Xie
A contrastive loss is defined to learn graph encoders by judging whether two augmented graphs are from the same original graph.
1 code implementation • 19 Jun 2020 • Xingyi Yang, Xuehai He, Yuxiao Liang, Yue Yang, Shanghang Zhang, Pengtao Xie
There has not been a clear understanding on what properties of data and tasks render one approach outperforms the other.
1 code implementation • 17 Jun 2020 • Xingyi Yang, Nandiraju Gireesh, Eric Xing, Pengtao Xie
To address this problem, we develop methods to generate view-consistent, high-fidelity, and high-resolution X-ray images from radiology reports to facilitate radiology training of medical students.
1 code implementation • 16 Jun 2020 • Ishika Singh, Haoyi Zhou, Kunlin Yang, Meng Ding, Bill Lin, Pengtao Xie
To address this problem, we propose federated neural architecture search (FNAS), where different parties collectively search for a differentiable architecture by exchanging gradients of architecture variables without exposing their data to other parties.
1 code implementation • 16 May 2020 • Hongchao Fang, Sicheng Wang, Meng Zhou, Jiayuan Ding, Pengtao Xie
We evaluate CERT on 11 natural language understanding tasks in the GLUE benchmark where CERT outperforms BERT on 7 tasks, achieves the same performance as BERT on 2 tasks, and performs worse than BERT on 2 tasks.
1 code implementation • 11 May 2020 • Wenmian Yang, Guangtao Zeng, Bowen Tan, Zeqian Ju, Subrato Chakravorty, Xuehai He, Shu Chen, Xingyi Yang, Qingyang Wu, Zhou Yu, Eric Xing, Pengtao Xie
On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT.
1 code implementation • medRxiv 2020 • Xuehai He, Xingyi Yang, Shanghang Zhang, Jinyu Zhao, Yichen Zhang, Eric Xing, Pengtao Xie
Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain.
1 code implementation • arXiv 2020 • Xuehai He, Shu Chen, Zeqian Ju, Xiangyu Dong, Hongchao Fang, Sicheng Wang, Yue Yang, Jiaqi Zeng, Ruisi Zhang, Ruoyu Zhang, Meng Zhou, Penghui Zhu, Pengtao Xie
Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.
no code implementations • 4 Apr 2020 • Yuxiao Liang, Pengtao Xie
Coronavirus disease 2019 (COVID-19) has infected more than one million individuals all over the world and caused more than 55, 000 deaths, as of April 3 in 2020.
19 code implementations • 30 Mar 2020 • Xingyi Yang, Xuehai He, Jinyu Zhao, Yichen Zhang, Shanghang Zhang, Pengtao Xie
Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0. 90, an AUC of 0. 98, and an accuracy of 0. 89.
6 code implementations • 7 Mar 2020 • Xuehai He, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie
To achieve this goal, the first step is to create a visual question answering (VQA) dataset where the AI agent is presented with a pathology image together with a question and is asked to give the correct answer.
1 code implementation • NeurIPS 2019 • Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour
The learned SSCM gives the specific causal knowledge for each individual as well as the general trend over the population.
no code implementations • 28 Sep 2019 • Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric Xing
The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses.
no code implementations • 25 Sep 2019 • Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing
Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system.
no code implementations • 28 May 2019 • Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P. Xing
In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain.
3 code implementations • 19 Feb 2019 • Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing
Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system.
no code implementations • 20 Nov 2018 • Xiangan Liu, Keyang Xu, Pengtao Xie, Eric Xing
Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs).
no code implementations • ICLR 2019 • Hongyang Zhang, Susu Xu, Jiantao Jiao, Pengtao Xie, Ruslan Salakhutdinov, Eric P. Xing
In this work, we give new results on the benefits of multi-generator architecture of GANs.
no code implementations • 31 Oct 2018 • Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao, Charlotte Band, Piyush Mathur, Frank Papay, Ashish K. Khanna, Jacek B. Cywinski, Kamal Maheshwari, Pengtao Xie, Eric Xing
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes.
no code implementations • 5 Aug 2018 • Hongbao Zhang, Pengtao Xie, Eric Xing
In this paper, we propose a probabilistic framework based on deep generative models for MVI.
no code implementations • ICML 2018 • Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric Xing
Variable selection is a classic problem in machine learning (ML), widely used to find important explanatory factors, and improve generalization performance and interpretability of ML models.
no code implementations • ACL 2018 • Pengtao Xie, Eric Xing
The International Classification of Diseases (ICD) provides a hierarchy of diagnostic codes for classifying diseases.
no code implementations • ICML 2018 • Pengtao Xie, Wei Wu, Yichen Zhu, Eric P. Xing
In this paper, we address these three issues by (1) seeking convex relaxations of the original nonconvex problems so that the global optimal is guaranteed to be achievable; (2) providing a formal analysis on OPR's capability of promoting balancedness; (3) providing a theoretical analysis that directly reveals the relationship between OPR and generalization performance.
no code implementations • 6 Dec 2017 • Christy Li, Dimitris Konomis, Graham Neubig, Pengtao Xie, Carol Cheng, Eric Xing
The hope is that the tool can be used to reduce mis-diagnosis.
no code implementations • 25 Nov 2017 • Shuai Zhang, Jian-Xin Li, Pengtao Xie, Yingchun Zhang, Minglai Shao, Haoyi Zhou, Mengyi Yan
Similar to DNNs, a SKN is composed of multiple layers of hidden units, but each parameterized by a RKHS function rather than a finite-dimensional vector.
no code implementations • ICLR 2018 • Pengtao Xie, Hongbao Zhang, Eric P. Xing
In representation learning (RL), how to make the learned representations easy to interpret and less overfitted to training data are two important but challenging issues.
no code implementations • 23 Nov 2017 • Pengtao Xie, Jun Zhu, Eric P. Xing
We also extend our approach to "diversify" Bayesian nonparametric models where the number of components is infinite.
4 code implementations • ACL 2018 • Baoyu Jing, Pengtao Xie, Eric Xing
To cope with these challenges, we (1) build a multi-task learning framework which jointly performs the pre- diction of tags and the generation of para- graphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to generate long paragraphs.
2 code implementations • 21 Nov 2017 • Devendra Singh Sachan, Pengtao Xie, Mrinmaya Sachan, Eric P. Xing
We also show that BiLM weight transfer leads to a faster model training and the pretrained model requires fewer training examples to achieve a particular F1 score.
no code implementations • 12 Nov 2017 • Shiyue Zhang, Pengtao Xie, Dong Wang, Eric P. Xing
In hospital, physicians rely on massive clinical data to make diagnosis decisions, among which laboratory tests are one of the most important resources.
no code implementations • 11 Nov 2017 • Haoran Shi, Pengtao Xie, Zhiting Hu, Ming Zhang, Eric P. Xing
Considering the complicated and dedicated process to assign correct codes to each patient admission based on overall diagnosis, we propose a hierarchical deep learning model with attention mechanism which can automatically assign ICD diagnostic codes given written diagnosis.
no code implementations • 4 Nov 2017 • Yuan Yang, Pengtao Xie, Xin Gao, Carol Cheng, Christy Li, Hongbao Zhang, Eric Xing
Predicting discharge medications right after a patient being admitted is an important clinical decision, which provides physicians with guidance on what type of medication regimen to plan for and what possible changes on initial medication may occur during an inpatient stay.
no code implementations • ICCV 2017 • Pengtao Xie, Ruslan Salakhutdinov, Luntian Mou, Eric P. Xing
Experiments on the two datasets demonstrate the efficacy and efficiency of the proposed methods.
no code implementations • ICML 2017 • Pengtao Xie, Aarti Singh, Eric P. Xing
Latent space models (LSMs) provide a principled and effective way to extract hidden patterns from observed data.
no code implementations • ICML 2017 • Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yao-Liang Yu, James Zou, Eric P. Xing
The large model capacity of latent space models (LSMs) enables them to achieve great performance on various applications, but meanwhile renders LSMs to be prone to overfitting.
no code implementations • ACL 2017 • Pengtao Xie, Eric Xing
Reading comprehension (RC), aiming to understand natural texts and answer questions therein, is a challenging task.
no code implementations • 11 Jun 2017 • Hao Zhang, Zeyu Zheng, Shizhen Xu, Wei Dai, Qirong Ho, Xiaodan Liang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Eric P. Xing
We show that Poseidon enables Caffe and TensorFlow to achieve 15. 5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification.
no code implementations • 31 Dec 2015 • Eric P. Xing, Qirong Ho, Pengtao Xie, Wei Dai
Taking the view that Big ML systems can benefit greatly from ML-rooted statistical and algorithmic insights --- and that ML researchers should therefore not shy away from such systems design --- we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions.
no code implementations • 23 Dec 2015 • Pengtao Xie, Yuntian Deng, Eric Xing
On two popular latent variable models --- restricted Boltzmann machine and distance metric learning, we demonstrate that MAR can effectively capture long-tail patterns, reduce model complexity without sacrificing expressivity and improve interpretability.
no code implementations • 19 Dec 2015 • Hao Zhang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Gunhee Kim, Qirong Ho, Eric Xing
To investigate how to adapt existing frameworks to efficiently support distributed GPUs, we propose Poseidon, a scalable system architecture for distributed inter-machine communication in existing DL frameworks.
no code implementations • 9 Dec 2015 • Abhimanu Kumar, Pengtao Xie, Junming Yin, Eric P. Xing
We propose a distributed approach to train deep neural networks (DNNs), which has guaranteed convergence theoretically and great scalability empirically: close to 6 times faster on instance of ImageNet data set when run with 6 machines.
no code implementations • 26 Nov 2015 • Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yao-Liang Yu, Eric Xing
Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology.
no code implementations • 23 Nov 2015 • Pengtao Xie, Yuntian Deng, Eric Xing
Recently diversity-inducing regularization methods for latent variable models (LVMs), which encourage the components in LVMs to be diverse, have been studied to address several issues involved in latent variable modeling: (1) how to capture long-tail patterns underlying data; (2) how to reduce model complexity without sacrificing expressivity; (3) how to improve the interpretability of learned patterns.
no code implementations • 19 Dec 2014 • Pengtao Xie, Eric Xing
In this paper, we propose Cauchy Principal Component Analysis (Cauchy PCA), a very simple yet effective PCA method which is robust to various types of noise.
1 code implementation • 18 Dec 2014 • Pengtao Xie, Misha Bilenko, Tom Finley, Ran Gilad-Bachrach, Kristin Lauter, Michael Naehrig
To achieve the privacy requirements, we use homomorphic encryption in the following protocol: the data owner encrypts the data and sends the ciphertexts to the third party to obtain a prediction from a trained model.
no code implementations • 18 Dec 2014 • Pengtao Xie, Eric Xing
In large scale machine learning and data mining problems with high feature dimensionality, the Euclidean distance between data points can be uninformative, and Distance Metric Learning (DML) is often desired to learn a proper similarity measure (using side information such as example data pairs being similar or dissimilar).
no code implementations • 19 Sep 2014 • Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yao-Liang Yu, Eric Xing
Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology.
no code implementations • 30 Dec 2013 • Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yao-Liang Yu
What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)?
no code implementations • 26 Sep 2013 • Pengtao Xie, Eric P. Xing
Document clustering and topic modeling are two closely related tasks which can mutually benefit each other.