1 code implementation • 26 Mar 2024 • Jin Peng Zhou, Charles Staats, Wenda Li, Christian Szegedy, Kilian Q. Weinberger, Yuhuai Wu
Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems.
no code implementations • 5 Feb 2024 • Ruihan Wu, Siddhartha Datta, Yi Su, Dheeraj Baby, Yu-Xiang Wang, Kilian Q. Weinberger
This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challenging.
no code implementations • 5 Feb 2024 • Quang-Huy Nguyen, Jin Peng Zhou, Zhenzhen Liu, Khanh-Huyen Bui, Kilian Q. Weinberger, Dung D. Le
RONIN conditions the inpainting process with the predicted ID label, drawing the input object closer to the in-distribution domain.
no code implementations • 24 Oct 2023 • Zhenzhen Liu, Chao Wan, Varsha Kishore, Jin Peng Zhou, Minmin Chen, Kilian Q. Weinberger
The results show that CoBa is effective and efficient in reducing hallucination, and offers great adaptability and flexibility.
1 code implementation • 23 Oct 2023 • Tai-Yu Pan, Chenyang Ma, Tianle Chen, Cheng Perng Phoo, Katie Z Luo, Yurong You, Mark Campbell, Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train.
no code implementations • 21 Sep 2023 • Travis Zhang, Katie Luo, Cheng Perng Phoo, Yurong You, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
Additionally, we leverage the statistics for a novel self-training process to stabilize the training.
1 code implementation • 23 Jul 2023 • Paloma Sodhi, Felix Wu, Ethan R. Elenberg, Kilian Q. Weinberger, Ryan Mcdonald
A common training technique for language models is teacher forcing (TF).
1 code implementation • 19 Jul 2023 • Varsha Kishore, Chao Wan, Justin Lovelace, Yoav Artzi, Kilian Q. Weinberger
Differentiable Search Index is a recently proposed paradigm for document retrieval, that encodes information about a corpus of documents within the parameters of a neural network and directly maps queries to corresponding documents.
1 code implementation • 27 Mar 2023 • Yurong You, Cheng Perng Phoo, Katie Z Luo, Travis Zhang, Wei-Lun Chao, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts.
1 code implementation • 20 Feb 2023 • Zhenzhen Liu, Jin Peng Zhou, Yufan Wang, Kilian Q. Weinberger
We present a novel approach for this task - Lift, Map, Detect (LMD) - that leverages recent advancement in diffusion models.
no code implementations • 20 Dec 2022 • Boyi Li, Rodolfo Corona, Karttikeya Mangalam, Catherine Chen, Daniel Flaherty, Serge Belongie, Kilian Q. Weinberger, Jitendra Malik, Trevor Darrell, Dan Klein
Are multimodal inputs necessary for grammar induction?
1 code implementation • NeurIPS 2023 • Justin Lovelace, Varsha Kishore, Chao Wan, Eliot Shekhtman, Kilian Q. Weinberger
We then demonstrate that continuous diffusion models can be learned in the latent space of the language autoencoder, enabling us to sample continuous latent representations that can be decoded into natural language with the pretrained decoder.
1 code implementation • 19 Oct 2022 • Ruihan Wu, Xiangyu Chen, Chuan Guo, Kilian Q. Weinberger
Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy.
no code implementations • CVPR 2022 • Carlos A. Diaz-Ruiz, Youya Xia, Yurong You, Jose Nino, Junan Chen, Josephine Monica, Xiangyu Chen, Katie Luo, Yan Wang, Marc Emond, Wei-Lun Chao, Bharath Hariharan, Kilian Q. Weinberger, Mark Campbell
Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions.
no code implementations • 16 Jun 2022 • Ruihan Wu, Xin Yang, Yuanshun Yao, Jiankai Sun, Tianyi Liu, Kilian Q. Weinberger, Chong Wang
Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects.
1 code implementation • NAACL 2022 • Ramya Ramakrishnan, Hashan Buddhika Narangodage, Mauro Schilman, Kilian Q. Weinberger, Ryan Mcdonald
This setting requires a model to not only consider the generation of these control words in the immediate context, but also produce utterances that will encourage the generation of the words at some time in the (possibly distant) future.
1 code implementation • 2 May 2022 • Felix Wu, Kwangyoun Kim, Shinji Watanabe, Kyu Han, Ryan Mcdonald, Kilian Q. Weinberger, Yoav Artzi
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data.
Ranked #3 on Named Entity Recognition (NER) on SLUE
Automatic Speech Recognition Automatic Speech Recognition (ASR) +7
2 code implementations • CVPR 2022 • Yurong You, Katie Z Luo, Cheng Perng Phoo, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
Current 3D object detectors for autonomous driving are almost entirely trained on human-annotated data.
1 code implementation • ICLR 2022 • Yurong You, Katie Z Luo, Xiangyu Chen, Junan Chen, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
Self-driving cars must detect vehicles, pedestrians, and other traffic participants accurately to operate safely.
1 code implementation • 25 Feb 2022 • Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger, Chuan Guo
Label differential privacy (label-DP) is a popular framework for training private ML models on datasets with public features and sensitive private labels.
1 code implementation • ICLR 2022 • Boyi Li, Kilian Q. Weinberger, Serge Belongie, Vladlen Koltun, René Ranftl
We present LSeg, a novel model for language-driven semantic image segmentation.
Ranked #1 on Few-Shot Semantic Segmentation on FSS-1000
1 code implementation • 20 Dec 2021 • Cole Miles, Rhine Samajdar, Sepehr Ebadi, Tout T. Wang, Hannes Pichler, Subir Sachdev, Mikhail D. Lukin, Markus Greiner, Kilian Q. Weinberger, Eun-Ah Kim
Specifically, we apply Hybrid-CCNN to analyze new quantum phases on square lattices with programmable interactions.
no code implementations • ICLR 2022 • Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger
In this paper, we investigate causes for this perceived instability.
1 code implementation • 14 Sep 2021 • Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi
This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • NeurIPS 2021 • Ruihan Wu, Chuan Guo, Yi Su, Kilian Q. Weinberger
Machine learning models often encounter distribution shifts when deployed in the real world.
no code implementations • NeurIPS 2021 • Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger
In this paper we investigate how RL agents are affected by exchanging the small MLPs with larger modern networks with skip connections and normalization, focusing specifically on actor-critic algorithms.
no code implementations • 26 Feb 2021 • Johan Bjorck, Xiangyu Chen, Christopher De Sa, Carla P. Gomes, Kilian Q. Weinberger
Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning.
1 code implementation • 9 Feb 2021 • Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger
We develop a novel approach for paper bidding and assignment that is much more robust against such attacks.
1 code implementation • 6 Nov 2020 • Cole Miles, Annabelle Bohrdt, Ruihan Wu, Christie Chiu, Muqing Xu, Geoffrey Ji, Markus Greiner, Kilian Q. Weinberger, Eugene Demler, Eun-Ah Kim
Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states.
1 code implementation • ICCV 2021 • Luyu Yang, Yan Wang, Mingfei Gao, Abhinav Shrivastava, Kilian Q. Weinberger, Wei-Lun Chao, Ser-Nam Lim
To integrate the strengths of the two classifiers, we apply the well-established co-training framework, in which the two classifiers exchange their high confident predictions to iteratively "teach each other" so that both classifiers can excel in the target domain.
Semi-supervised Domain Adaptation Unsupervised Domain Adaptation
1 code implementation • NeurIPS 2020 • Divyansh Garg, Yan Wang, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values.
Ranked #2 on Stereo Depth Estimation on KITTI2015 (three pixel error metric)
3D Object Detection From Stereo Images Autonomous Driving +5
1 code implementation • ICLR 2021 • Tianyi Zhang, Felix Wu, Arzoo Katiyar, Kilian Q. Weinberger, Yoav Artzi
We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process.
1 code implementation • CVPR 2020 • Yan Wang, Xiangyu Chen, Yurong You, Li Erran, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike.
1 code implementation • CVPR 2020 • Rui Qian, Divyansh Garg, Yan Wang, Yurong You, Serge Belongie, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao
Reliable and accurate 3D object detection is a necessity for safe autonomous driving.
1 code implementation • CVPR 2021 • Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, Kilian Q. Weinberger
The moments (a. k. a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time.
Ranked #32 on Domain Generalization on ImageNet-A
no code implementations • 24 Feb 2020 • Chuan Guo, Ruihan Wu, Kilian Q. Weinberger
Modern neural networks often contain significantly more parameters than the size of their training data.
no code implementations • 3 Feb 2020 • Wei-Lun Chao, Han-Jia Ye, De-Chuan Zhan, Mark Campbell, Kilian Q. Weinberger
Recent years have witnessed an abundance of new publications and approaches on meta-learning.
2 code implementations • NeurIPS 2020 • Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger
Not all data in a typical training set help with generalization; some samples can be overly ambiguous or outrightly mislabeled.
no code implementations • 8 Jan 2020 • Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
no code implementations • ICLR 2020 • Chuan Guo, Ruihan Wu, Kilian Q. Weinberger
The complexity of large-scale neural networks can lead to poor understanding of their internal details.
6 code implementations • 12 Nov 2019 • Yan Wang, Wei-Lun Chao, Kilian Q. Weinberger, Laurens van der Maaten
Few-shot learners aim to recognize new object classes based on a small number of labeled training examples.
1 code implementation • 30 Oct 2019 • Brian H. Wang, Wei-Lun Chao, Yan Wang, Bharath Hariharan, Kilian Q. Weinberger, Mark Campbell
We obtain 2-D segmentation predictions by applying Mask-RCNN to the RGB image, and then link this image to a 3-D lidar point cloud by building a graph of connections among 3-D points and 2-D pixels.
1 code implementation • NeurIPS 2019 • Tao Yu, Shengyuan Hu, Chuan Guo, Wei-Lun Chao, Kilian Q. Weinberger
Natural images are virtually surrounded by low-density misclassified regions that can be efficiently discovered by gradient-guided search --- enabling the generation of adversarial images.
no code implementations • 28 Sep 2019 • Felix Wu, Boyi Li, Lequn Wang, Ni Lao, John Blitzer, Kilian Q. Weinberger
This paper introduces Integrated Triaging, a framework that prunes almost all context in early layers of a network, leaving the remaining (deep) layers to scan only a tiny fraction of the full corpus.
no code implementations • 25 Sep 2019 • Geoff Pleiss, Amauri Souza, Joseph Kim, Boyi Li, Kilian Q. Weinberger
Neural network out-of-distribution (OOD) detection aims to identify when a model is unable to generalize to new inputs, either due to covariate shift or anomalous data.
Out-of-Distribution Detection Out of Distribution (OOD) Detection +1
no code implementations • 25 Sep 2019 • Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger
This paper introduces a new method to discover mislabeled training samples and to mitigate their impact on the training process of deep networks.
2 code implementations • NeurIPS 2019 • Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge Belongie
A popular method to reduce the training time of deep neural networks is to normalize activations at each layer.
1 code implementation • ICLR 2020 • Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation.
3D Object Detection From Stereo Images Autonomous Driving +2
4 code implementations • ICLR 2019 • Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger
We propose an intriguingly simple method for the construction of adversarial images in the black-box setting.
15 code implementations • ICLR 2020 • Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, Yoav Artzi
We propose BERTScore, an automatic evaluation metric for text generation.
3 code implementations • NeurIPS 2019 • Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data.
2 code implementations • 28 Feb 2019 • Felix Wu, Boyi Li, Lequn Wang, Ni Lao, John Blitzer, Kilian Q. Weinberger
In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12].
7 code implementations • 19 Feb 2019 • Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
Ranked #3 on Text Classification on Ohsumed
no code implementations • 13 Jan 2019 • Zhixiang Eddie Xu, Gao Huang, Kilian Q. Weinberger, Alice X. Zheng
A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of features and dimensions; allow the incorporation of known sparsity structure.
no code implementations • 13 Jan 2019 • Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Alice X. Zheng
As machine learning transitions increasingly towards real world applications controlling the test-time cost of algorithms becomes more and more crucial.
2 code implementations • CVPR 2019 • Yan Wang, Wei-Lun Chao, Divyansh Garg, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference.
3D Object Detection From Stereo Images Autonomous Driving +2
3 code implementations • 26 Oct 2018 • Yan Wang, Zihang Lai, Gao Huang, Brian H. Wang, Laurens van der Maaten, Mark Campbell, Kilian Q. Weinberger
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.
Ranked #1 on Stereo Depth Estimation on KITTI2012
no code implementations • 19 Oct 2018 • Brian H. Wang, Yan Wang, Kilian Q. Weinberger, Mark Campbell
We present a data association method for vision-based multiple pedestrian tracking, using deep convolutional features to distinguish between different people based on their appearances.
4 code implementations • NeurIPS 2018 • Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson
Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware.
1 code implementation • 24 Sep 2018 • Chuan Guo, Jared S. Frank, Kilian Q. Weinberger
In this paper we propose to restrict the search for adversarial images to a low frequency domain.
no code implementations • NeurIPS 2018 • Johan Bjorck, Carla Gomes, Bart Selman, Kilian Q. Weinberger
Batch normalization (BN) is a technique to normalize activations in intermediate layers of deep neural networks.
1 code implementation • CVPR 2018 • Yan Wang, Lequn Wang, Yurong You, Xu Zou, Vincent Chen, Serena Li, Gao Huang, Bharath Hariharan, Kilian Q. Weinberger
Not all people are equally easy to identify: color statistics might be enough for some cases while others might require careful reasoning about high- and low-level details.
Ranked #12 on Person Re-Identification on CUHK03 detected
1 code implementation • ICML 2018 • Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
One of the most compelling features of Gaussian process (GP) regression is its ability to provide well-calibrated posterior distributions.
1 code implementation • 24 Feb 2018 • Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson
Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs).
6 code implementations • CVPR 2018 • Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger
It combines dense connectivity with a novel module called learned group convolution.
1 code implementation • NeurIPS 2017 • Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, Kilian Q. Weinberger
The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models.
6 code implementations • 21 Jul 2017 • Geoff Pleiss, Danlu Chen, Gao Huang, Tongcheng Li, Laurens van der Maaten, Kilian Q. Weinberger
A 264-layer DenseNet (73M parameters), which previously would have been infeasible to train, can now be trained on a single workstation with 8 NVIDIA Tesla M40 GPUs.
17 code implementations • ICML 2017 • Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications.
10 code implementations • 1 Apr 2017 • Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger
In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost.
7 code implementations • ICLR 2018 • Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger
In this paper we investigate image classification with computational resource limits at test time.
1 code implementation • NeurIPS 2016 • Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger
Accurately measuring the similarity between text documents lies at the core of many real world applications of machine learning.
144 code implementations • CVPR 2017 • Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
Ranked #1 on Classification on XImageNet-12
no code implementations • 17 Dec 2015 • Matt J. Kusner, Yu Sun, Karthik Sridharan, Kilian Q. Weinberger
Causal inference has the potential to have significant impact on medical research, prevention and control of diseases, and identifying factors that impact economic changes to name just a few.
no code implementations • NeurIPS 2015 • Jacob Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham
Using this and a previously published model for healthy responses, the proposed method is shown to be capable of diagnosing the presence or absence of NIHL with drastically fewer samples than existing approaches.
no code implementations • NeurIPS 2015 • Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley
Clustering large data is a fundamental problem with a vast number of applications.
no code implementations • 19 Nov 2015 • Jacob R. Gardner, Paul Upchurch, Matt J. Kusner, Yixuan Li, Kilian Q. Weinberger, Kavita Bala, John E. Hopcroft
Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in order to alter the class membership.
no code implementations • 14 Jun 2015 • Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen
Convolutional neural networks (CNN) are increasingly used in many areas of computer vision.
1 code implementation • 19 Apr 2015 • Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen
As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models.
no code implementations • 26 Jan 2015 • Zhixiang Xu, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger
For most of the time during which we conducted this research, we were unaware of this prior work.
no code implementations • 16 Jan 2015 • Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger
The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems.
no code implementations • 4 Dec 2014 • Matt J. Kusner, Nicholas I. Kolkin, Stephen Tyree, Kilian Q. Weinberger
Specifically, we show that we can reduce data sets to 16% and in some cases as little as 2% of their original size, while approximately matching the test error of kNN classification on the full training set.
no code implementations • 6 Sep 2014 • Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen
Our second contribution is to derive a practical algorithm based on this reduction.
no code implementations • 3 Apr 2014 • Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran
In particular, we provide the first comparison of algorithms with explicit and implicit parallelization.
no code implementations • NeurIPS 2012 • Dor Kedem, Stephen Tyree, Fei Sha, Gert R. Lanckriet, Kilian Q. Weinberger
On various benchmark data sets, we demonstrate these methods not only match the current state-of-the-art in terms of kNN classification error, but in the case of χ2-LMNN, obtain best results in 19 out of 20 learning settings.
no code implementations • 9 Oct 2012 • Zhixiang Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen
Recently, machine learning algorithms have successfully entered large-scale real-world industrial applications (e. g. search engines and email spam filters).
no code implementations • NeurIPS 2011 • Minmin Chen, Kilian Q. Weinberger, John Blitzer
Our algorithm is a variant of co-training, and we name it CODA (Co-training for domain adaptation).
no code implementations • NeurIPS 2010 • Shibin Parameswaran, Kilian Q. Weinberger
Multi-task learning (MTL) improves the prediction performance on multiple, different but related, learning problems through shared parameters or representations.
no code implementations • NeurIPS 2010 • Yuzong Liu, Mohit Sharma, Charles Gaona, Jonathan Breshears, Jarod Roland, Zachary Freudenburg, Eric Leuthardt, Kilian Q. Weinberger
For successful upper limb BCIs, it is important to decode finger movements from brain activity.
no code implementations • NeurIPS 2008 • Kilian Q. Weinberger, Olivier Chapelle
The optimization of the semantic space incorporates large margin constraints that ensure that for each instance the correct class prototype is closer than any other.