Jay Yagnik
Jay Yagnik is currently a Vice President and Engineering Fellow at Google, leading large parts of Google AI. While at Google he has led many foundational research efforts in machine learning and perception, computer vision, video understanding, privacy preserving machine learning, quantum AI, applied sciences, and more. He also created multiple engineering and product successes for the company, in areas including Google Photos, YouTube, Search, Ads, Android, Maps, and Hardware. Jay’s research interests span the fields of deep learning, reinforcement learning, scalable matching, graph information propagation, image representation and recognition, temporal information mining, and sparse networks.
Jay is an alumnus of the Indian Institute of Science and the Institute of Technology, Nirma University for graduate and undergraduate studies.
Authored Publications
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SmartChoices: Hybridizing Programming and Machine Learning
Alexander Daryin
Thomas Deselaers
Nikhil Sarda
Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 36th International Conference on Machine Learning (ICML), (2019)
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We present SmartChoices, an approach to making machine learning (ML) a first class citizen in programming languages which we see as one way to lower the entrance cost to applying ML to problems in new domains. There is a growing divide in approaches to building systems: on the one hand, programming leverages human experts to define a system while on the other hand behavior is learned from data in machine learning. We propose to hybridize these two by providing a 3-call API which we expose through an object called SmartChoice. We describe the SmartChoices-interface, how it can be used in programming with minimal code changes, and demonstrate that it is an easy to use but still powerful tool by demonstrating improvements over not using ML at all on three algorithmic problems: binary search, QuickSort, and caches. In these three examples, we replace the commonly used heuristics with an ML model entirely encapsulated within a SmartChoice and thus requiring minimal code changes. As opposed to previous work applying ML to algorithmic problems, our proposed approach does not require to drop existing implementations but seamlessly integrates into the standard software development workflow and gives full control to the software developer over how ML methods are applied. Our implementation relies on standard Reinforcement Learning (RL) methods. To learn faster, we use the heuristic function, which they are replacing, as an initial function. We show how this initial function can be used to speed up and stabilize learning while providing a safety net that prevents performance to become substantially worse -- allowing for a safe deployment in critical applications in real life.
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Identifying interesting portions of videos
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Arthur Wait
Krishna Bharat
Christian Frueh
Ryan Michael Hickman
Patent (2019)
Deep Networks With Large Output Spaces
Jonathon Shlens
Rajat Monga
International Conference on Learning Representations (2015)
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Deep neural networks have been extremely successful at various image, speech, video recognition tasks because of their ability to model deep structures within the data. However, they are still prohibitively expensive to train and apply for problems containing millions of classes in the output layer. Based on the observation that the key computation common to most neural network layers is a vector/matrix product, we propose a fast locality-sensitive hashing technique to approximate the actual dot product enabling us to scale up the training and inference to millions of output classes. We evaluate our technique on three diverse large-scale recognition tasks and show that our approach can train large-scale models at a faster rate (in terms of steps/total time) compared to baseline methods.
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Fast, Accurate Detection of 100,000 Object Classes on a Single Machine: Technical Supplement
Thomas Dean
Mark Ruzon
Mark Segal
Jonathon Shlens
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington, DC, USA (2013)
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In the companion paper published in CVPR 2013, we presented a method that can directly use deformable part models (DPMs) trained as in [Felzenszwalb et al CVPR 2008]. After training, HOG based part filters are hashed, and, during inference, counts of hashing collisions summed over all hash bands serve as a proxy for part-filter / sliding-window dot products, i.e., filter responses. These counts are an approximation and so we take the original HOG-based filters for the top hash counts and calculate the exact dot products for scoring.
It is possible to train DPM models not on HOG data but on a hashed WTA [Yagnik et al ICCV 2011] version of this data. The resulting part filters are sparse, real-valued vectors the size of WTA vectors computed from sliding windows. Given the WTA hash of a window, we exactly recover dot products of the top responses using an extension of locality-sensitive hashing. In this supplement, we sketch a method for training such WTA-based models.
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Discriminative Segment Annotation in Weakly Labeled Video
Kevin Tang
Li Fei-Fei
Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR 2013)
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paper tackles the problem of segment annotation in complex Internet videos. Given a weakly labeled video, we automatically generate spatiotemporal masks for each of the concepts with which it is labeled. This is a particularly relevant problem in the video domain, as large numbers of YouTube videos are now available, tagged with the visual concepts that they contain.
Given such weakly labeled videos, we focus on the problem of spatiotemporal segment classification. We propose a straightforward algorithm, CRANE, that utilizes large amounts of weakly labeled video to rank spatiotemporal segments by the likelihood that they correspond to a given visual concept. We make publicly available segment-level annotations for a subset of the Prest et al. dataset and show convincing results. We also show state-of-the-art results on Hartmann et al.'s more difficult, large-scale object segmentation dataset.
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Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
Thomas Dean
Mark Ruzon
Mark Segal
Jonathon Shlens
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington, DC, USA (2013)
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Many object detection systems are constrained by the time required to convolve a target image with a bank of filters that code for different aspects of an object's appearance, such as the presence of component parts. We exploit locality-sensitive hashing to replace the dot-product kernel operator in the convolution with a fixed number of hash-table probes that effectively sample all of the filter responses in time independent of the size of the filter bank. To show the effectiveness of the technique, we apply it to evaluate 100,000 deformable-part models requiring over a million (part) filters on multiple scales of a target image in less than 20 seconds using a single multi-core processor with 20GB of RAM. This represents a speed-up of approximately 20,000 times - four orders of magnitude - when compared with performing the convolutions explicitly on the same hardware. While mean average precision over the full set of 100,000 object classes is around 0.16 due in large part to the challenges in gathering training data and collecting ground truth for so many classes, we achieve a mAP of at least 0.20 on a third of the classes and 0.30 or better on about 20% of the classes.
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The Power of Comparative Reasoning
Dennis Strelow
Ruei-Sung Lin
International Conference on Computer Vision, IEEE (2011)
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Rank correlation measures are known for their resilience
to perturbations in numeric values and are widely used
in many evaluation metrics. Such ordinal measures have
rarely been applied in treatment of numeric features as a
representational transformation. We emphasize the benefits
of ordinal representations of input features both theoretically and empirically. We present a family of algorithms for computing ordinal embeddings based on partial order statistics. Apart from having the stability benefits of ordinal measures, these embeddings are highly nonlinear, giving rise to sparse feature spaces highly favored by several machine learning methods. These embeddings are deterministic, data independent and by virtue of being based on partial order statistics, add another degree of resilience to noise. These machine-learning-free methods when applied to the task of fast similarity search outperform state-of-theart machine learning methods with complex optimization setups. For solving classification problems, the embeddings provide a nonlinear transformation resulting in sparse binary codes that are well-suited for a large class of machine learning algorithms. These methods show significant improvement on VOC 2010 using simple linear classifiers which can be trained quickly. Our method can be extended to the case of polynomial kernels, while permitting very efficient computation. Further, since the popular MinHash algorithm is a special case of our method, we demonstrate an efficient scheme for computing MinHash on conjunctions of binary features. The actual method can be implemented in about 10 lines of code in most languages (2 lines in MATLAB), and does not require any data-driven optimization.
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Finding Meaning on YouTube: Tag Recommendation and Category Discovery
Marius Pasca
Computer Vision and Pattern Recognition, IEEE (2010)
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We present a system that automatically recommends tags for YouTube
videos solely based on their audiovisual content. We also propose a novel framework
for unsupervised discovery of video categories that exploits knowledge mined
from the World-Wide Web text documents/searches. First, video content to tag
association is learned by training classifiers that map audiovisual
content-based features from millions of videos on YouTube.com to existing
uploader-supplied tags for these videos. When a new video is uploaded, the
labels provided by these classifiers are used to automatically suggest tags
deemed relevant to the video. Our system has learned a vocabulary of over 20,000 tags.
Secondly, we mined large volumes of Web pages and search queries to discover a
set of possible text entity categories and a set of associated is-A
relationships that map individual text entities to categories. Finally, we
apply these is-A relationships mined from web text on the tags learned from
audiovisual content of videos to automatically synthesize a reliable set of
categories most relevant to videos -- along with a mechanism to predict these
categories for new uploads. We then present rigorous rating studies that
establish that: (a) the average relevance of tags automatically recommended by
our system matches the average relevance of the uploader-supplied tags at the
same or better coverage and (b) the average precision@K of video categories
discovered by our system is 70% with K=5.
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SPEC Hashing: Similarity Preserving algorithm for Entropy-based Coding
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Ruei-Sung Lin
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)
A Large-Scale Taxonomic Classification System for Web-based Videos
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Reto Strobl
John Zhang
the 11th European Conference on Computer Vision (ECCV 2010)
