1 code implementation • 16 Feb 2024 • Alberto Cabezas, Adrien Corenflos, Junpeng Lao, Rémi Louf, Antoine Carnec, Kaustubh Chaudhari, Reuben Cohn-Gordon, Jeremie Coullon, Wei Deng, Sam Duffield, Gerardo Durán-Martín, Marcin Elantkowski, Dan Foreman-Mackey, Michele Gregori, Carlos Iguaran, Ravin Kumar, Martin Lysy, Kevin Murphy, Juan Camilo Orduz, Karm Patel, Xi Wang, Rob Zinkov
BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation.
no code implementations • NeurIPS 2023 • Lijun Yu, Yong Cheng, Zhiruo Wang, Vivek Kumar, Wolfgang Macherey, Yanping Huang, David A. Ross, Irfan Essa, Yonatan Bisk, Ming-Hsuan Yang, Kevin Murphy, Alexander G. Hauptmann, Lu Jiang
In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos.
1 code implementation • 31 May 2023 • Peter G. Chang, Gerardo Durán-Martín, Alexander Y Shestopaloff, Matt Jones, Kevin Murphy
We propose an efficient online approximate Bayesian inference algorithm for estimating the parameters of a nonlinear function from a potentially non-stationary data stream.
4 code implementations • 2 Jan 2023 • Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T. Freeman, Michael Rubinstein, Yuanzhen Li, Dilip Krishnan
Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding.
Ranked #1 on Text-to-Image Generation on MS-COCO (FID metric)
1 code implementation • 28 Nov 2022 • Qingyao Sun, Kevin Murphy, Sayna Ebrahimi, Alexander D'Amour
However, we assume that the generative model for features $p(x|y, z)$ is invariant across domains.
no code implementations • 20 Oct 2022 • Zeel B Patel, Nipun Batra, Kevin Murphy
Gaussian processes are Bayesian non-parametric models used in many areas.
1 code implementation • 21 Jul 2022 • David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A. Saurous, Jascha Sohl-Dickstein, Kevin Murphy, Charles Sutton
Prompted models have demonstrated impressive few-shot learning abilities.
1 code implementation • 15 Jul 2022 • Dustin Tran, Jeremiah Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures.
1 code implementation • 1 Dec 2021 • Gerardo Duran-Martin, Aleyna Kara, Kevin Murphy
In this paper we present a new algorithm for online (sequential) inference in Bayesian neural networks, and show its suitability for tackling contextual bandit problems.
3 code implementations • 7 Jun 2021 • Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Qixuan Feng, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Faris Sbahi, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran
In this paper we introduce Uncertainty Baselines: high-quality implementations of standard and state-of-the-art deep learning methods on a variety of tasks.
1 code implementation • 17 Apr 2021 • Kevin Murphy, Abhishek Kumar, Stylianos Serghiou
Although this data is already being collected (in an aggregated, privacy-preserving way) by several health authorities, in this paper we limit ourselves to simulated data, so that we can systematically study the different factors that affect the feasibility of the approach.
1 code implementation • 27 Oct 2020 • Joao Ramos, Yanran Ding, Young-woo Sim, Kevin Murphy, Daniel Block
This letter introduces HOPPY, an open-source, low-cost, robust, and modular kit for robotics education.
Robotics
no code implementations • ICML 2020 • Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D. Sculley
The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds of large batches of sequences--a setting that off-the-shelf black-box optimization methods are ill-equipped to handle.
1 code implementation • 7 May 2020 • Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy
The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning.
no code implementations • ICLR 2020 • Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy Colwell
In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 24 Apr 2020 • Michael Zhu, Kevin Murphy, Rico Jonschkowski
Resampling is a key component of sample-based recursive state estimation in particle filters.
no code implementations • 20 Feb 2020 • Abhishek Kumar, Ben Poole, Kevin Murphy
Invertible flow-based generative models are an effective method for learning to generate samples, while allowing for tractable likelihood computation and inference.
1 code implementation • CVPR 2020 • Junwei Liang, Lu Jiang, Kevin Murphy, Ting Yu, Alexander Hauptmann
The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals.
Ranked #1 on Multi-future Trajectory Prediction on ForkingPaths
1 code implementation • NeurIPS 2019 • Matthias Minderer, Chen Sun, Ruben Villegas, Forrester Cole, Kevin Murphy, Honglak Lee
Extracting and predicting object structure and dynamics from videos without supervision is a major challenge in machine learning.
Ranked #11 on Video Prediction on KTH
2 code implementations • NeurIPS 2019 • Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn
We find that, using our approach, agents can learn to solve to diverse, temporally-extended tasks such as object sorting and multi-object rearrangement, including from raw pixel observations.
1 code implementation • 16 Jun 2019 • Steven Hickson, Karthik Raveendran, Alireza Fathi, Kevin Murphy, Irfan Essa
We propose 4 insights that help to significantly improve the performance of deep learning models that predict surface normals and semantic labels from a single RGB image.
Ranked #1 on Semantic Segmentation on ScanNetV2 (Pixel Accuracy metric)
no code implementations • 13 Jun 2019 • Chen Sun, Fabien Baradel, Kevin Murphy, Cordelia Schmid
This paper proposes a self-supervised learning approach for video features that results in significantly improved performance on downstream tasks (such as video classification, captioning and segmentation) compared to existing methods.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 24 May 2019 • David H. Brookes, Akosua Busia, Clara Fannjiang, Kevin Murphy, Jennifer Listgarten
We show that a large class of Estimation of Distribution Algorithms, including, but not limited to, Covariance Matrix Adaption, can be written as a Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of infinite samples.
no code implementations • ICLR 2019 • Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu
Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future.
no code implementations • ICLR 2019 • Chen Sun, Per Karlsson, Jiajun Wu, Joshua B. Tenenbaum, Kevin Murphy
We present a method which learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents.
no code implementations • CVPR 2019 • Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, Cordelia Schmid
This paper focuses on multi-person action forecasting in videos.
3 code implementations • ICCV 2019 • Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid
Self-supervised learning has become increasingly important to leverage the abundance of unlabeled data available on platforms like YouTube.
Ranked #1 on Action Classification on YouCook2
no code implementations • 12 Mar 2019 • Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu
Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future.
no code implementations • 25 Feb 2019 • Chen Sun, Per Karlsson, Jiajun Wu, Joshua B. Tenenbaum, Kevin Murphy
We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents.
4 code implementations • 25 Feb 2019 • Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, Frank Hutter
Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation.
4 code implementations • CVPR 2019 • Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, James Hays
In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image.
Ranked #2 on Image Retrieval with Multi-Modal Query on MIT-States
1 code implementation • 30 Sep 2018 • Seong Joon Oh, Kevin Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, Andrew Gallagher
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.
1 code implementation • ECCV 2018 • Chen Sun, Abhinav Shrivastava, Carl Vondrick, Kevin Murphy, Rahul Sukthankar, Cordelia Schmid
A visualization of the learned relation features confirms that our approach is able to attend to the relevant relations for each action.
Ranked #15 on Action Recognition on AVA v2.1
1 code implementation • ECCV 2018 • Carl Vondrick, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, Kevin Murphy
We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision.
3 code implementations • ECCV 2018 • George Papandreou, Tyler Zhu, Liang-Chieh Chen, Spyros Gidaris, Jonathan Tompson, Kevin Murphy
We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model.
Ranked #8 on Multi-Person Pose Estimation on COCO test-dev
no code implementations • ICLR 2018 • Alex Alemi, Ben Poole, Ian Fischer, Josh Dillon, Rif A. Saurus, Kevin Murphy
We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference.
1 code implementation • ECCV 2018 • Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, Kevin Murphy
Despite the steady progress in video analysis led by the adoption of convolutional neural networks (CNNs), the relative improvement has been less drastic as that in 2D static image classification.
Ranked #27 on Action Recognition on UCF101 (using extra training data)
18 code implementations • ECCV 2018 • Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.
Ranked #15 on Neural Architecture Search on NAS-Bench-201, ImageNet-16-120 (Accuracy (Val) metric)
4 code implementations • ICLR 2018 • Amélie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, Kevin Murphy
Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter.
1 code implementation • ICML 2018 • Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy
Recent work in unsupervised representation learning has focused on learning deep directed latent-variable models.
no code implementations • ICLR 2018 • Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy
It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before.
no code implementations • 19 May 2017 • Sergio Guadarrama, Ryan Dahl, David Bieber, Mohammad Norouzi, Jonathon Shlens, Kevin Murphy
Then, given the generated low-resolution color image and the original grayscale image as inputs, we train a second CNN to generate a high-resolution colorization of an image.
Ranked #3 on Colorization on ImageNet val
3 code implementations • 11 Apr 2017 • Zbigniew Wojna, Alex Gorban, Dar-Shyang Lee, Kevin Murphy, Qian Yu, Yeqing Li, Julian Ibarz
We present a neural network model - based on CNNs, RNNs and a novel attention mechanism - which achieves 84. 2% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith'16), which achieved 72. 46%.
Ranked #1 on Optical Character Recognition (OCR) on FSNS - Test
no code implementations • 13 Jan 2017 • Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning.
1 code implementation • CVPR 2017 • Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik
We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation).
no code implementations • CVPR 2017 • George Papandreou, Tyler Zhu, Nori Kanazawa, Alexander Toshev, Jonathan Tompson, Chris Bregler, Kevin Murphy
Trained on COCO data alone, our final system achieves average precision of 0. 649 on the COCO test-dev set and the 0. 643 test-standard sets, outperforming the winner of the 2016 COCO keypoints challenge and other recent state-of-art.
Ranked #6 on Keypoint Detection on COCO test-challenge
1 code implementation • CVPR 2017 • Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, Kevin Murphy
Learning the representation and the similarity metric in an end-to-end fashion with deep networks have demonstrated outstanding results for clustering and retrieval.
2 code implementations • ICCV 2017 • Si-Qi Liu, Zhenhai Zhu, Ning Ye, Sergio Guadarrama, Kevin Murphy
Finally, we show that using our PG method we can optimize any of the metrics, including the proposed SPIDEr metric which results in image captions that are strongly preferred by human raters compared to captions generated by the same model but trained to optimize MLE or the COCO metrics.
9 code implementations • 1 Dec 2016 • Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, Kevin Murphy
We present a variational approximation to the information bottleneck of Tishby et al. (1999).
14 code implementations • CVPR 2017 • Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang song, Sergio Guadarrama, Kevin Murphy
On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.
Ranked #209 on Object Detection on COCO test-dev (using extra training data)
47 code implementations • 2 Jun 2016 • Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales.
no code implementations • 19 Nov 2015 • Jonathan Huang, Kevin Murphy
We present a generative model of images based on layering, in which image layers are individually generated, then composited from front to back.
no code implementations • CVPR 2016 • Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, Alan L. Yuille
Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems.
no code implementations • CVPR 2016 • Vignesh Ramanathan, Jonathan Huang, Sami Abu-El-Haija, Alexander Gorban, Kevin Murphy, Li Fei-Fei
In this paper, we propose a model which learns to detect events in such videos while automatically "attending" to the people responsible for the event.
1 code implementation • CVPR 2016 • Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan Yuille, Kevin Murphy
We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described.
no code implementations • 8 Sep 2015 • Nicholas Allgaier, Tobias Banaschewski, Gareth Barker, Arun L. W. Bokde, Josh C. Bongard, Uli Bromberg, Christian Büchel, Anna Cattrell, Patricia J. Conrod, Christopher M. Danforth, Sylvane Desrivières, Peter S. Dodds, Herta Flor, Vincent Frouin, Jürgen Gallinat, Penny Gowland, Andreas Heinz, Bernd Ittermann, Scott Mackey, Jean-Luc Martinot, Kevin Murphy, Frauke Nees, Dimitri Papadopoulos-Orfanos, Luise Poustka, Michael N. Smolka, Henrik Walter, Robert Whelan, Gunter Schumann, Hugh Garavan, IMAGEN Consortium
In the present study, we introduce just such a method, called nonlinear functional mapping (NFM), and demonstrate its application in the analysis of resting state fMRI from a 242-subject subset of the IMAGEN project, a European study of adolescents that includes longitudinal phenotypic, behavioral, genetic, and neuroimaging data.
1 code implementation • NeurIPS 2015 • Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities, e. g., for applications involving bandits or active learning.
1 code implementation • 5 Mar 2015 • Jonathan Malmaud, Jonathan Huang, Vivek Rathod, Nick Johnston, Andrew Rabinovich, Kevin Murphy
We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task.
no code implementations • ICCV 2015 • Nan Ding, Jia Deng, Kevin Murphy, Hartmut Neven
In this paper, we extend the HEX model to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels (e. g., an antelope is "sort of" furry, but not to the same degree as a grizzly bear).
2 code implementations • 2 Mar 2015 • Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich
In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph).
3 code implementations • 9 Feb 2015 • George Papandreou, Liang-Chieh Chen, Kevin Murphy, Alan L. Yuille
Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.
18 code implementations • 22 Dec 2014 • Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
This is due to the very invariance properties that make DCNNs good for high level tasks.
Ranked #3 on Scene Segmentation on SUN-RGBD
1 code implementation • 23 Jan 2013 • Kevin Murphy, Yair Weiss, Michael. I. Jordan
Recently, researchers have demonstrated that loopy belief propagation - the use of Pearls polytree algorithm IN a Bayesian network WITH loops OF error- correcting codes. The most dramatic instance OF this IS the near Shannon - limit performance OF Turbo Codes codes whose decoding algorithm IS equivalent TO loopy belief propagation IN a chain - structured Bayesian network.
no code implementations • 19 Jan 2013 • Nando de Freitas, Kevin Murphy
This is the Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, which was held on Catalina Island, CA August 14-18 2012.