Sravanti Addepalli, Indian Institute of Science Effective image representations are the key components required to solve instance-level recognition problems. We reduced the total number of parameters in the audio-visual model by replacing “regular” 2D convolutions with separable ones (1D in the frequency dimension, followed by 1D in the time dimension) with fewer filters. Because DeepVariant uses the same codebase for each data type, improvements apply to each of Illumina, PacBio, and Oxford Nanopore.

Further, the model needed to co-exist alongside other ML algorithms used in the YouTube app in addition to the resource-consuming video recording itself. We developed a lightweight, real-time, sign language detection web demo that connects to various video conferencing applications and can set the user as the “speaker” when they sign. Algorithms, Optimizations and Markets Acknowledgements

The DELG model leverages a fully-convolutional neural network with two different heads: one for global features and the other for local features. User Feedback To mitigate this, we use the sharding capability provided by Reverb to increase the throughput between actors, learner, and replay buffer services. The TPU performance is often limited by the efficiency of the input pipeline in feeding the training data to the TPU compute cores. Eric Heiden, University of Southern California BERT and ALBERT metrics on OntoNotes (accuracy) and WinoGender (gendered correlations). To deliver a high throughput input data pipeline, Menger uses Reverb, a recently open-sourced data storage system designed for machine learning applications that provides an efficient and flexible platform to implement experience replay in a variety of on-policy/off-policy algorithms.

DELG: DEep Local and Global Features We repeat the experiment when the replay buffer and actors are placed on the same Borg cell. DeepVariant v1.0 reduces Illumina errors by another ~22% and PacBio errors by another ~52% relative to the last DeepVariant release (v0.10). Explore the cutting-edge work Google is doing in AI and machine learning. We then optimized the model further using TensorFlow Lite — a set of tools that enable running TensorFlow models on mobile devices with low latency and a small binary size. 1 Zari is an Afghan Muppet designed to show that ‘a little girl could do as much as everybody else’. The sign language detection demo takes the webcam’s video feed as input, and transmits audio through a virtual microphone when it detects that the user is signing. We also put the technology through extensive testing to verify that it performs consistently across different recording conditions and for people with different appearances and voices.

The timeframe for the competition was compressed, so we trained only with data similar to the challenge data (PCR-Free NovaSeq) to speed model training. Menger reduces the training time by up to 8.6x compared to a baseline implementation. Amit Moryossef, Ioannis Tsochantaridis, Roee Aharoni, Sarah Ebling, Annette Rios, Srini Narayanan, George Sung, Jonathan Baccash, Aidan Bryant, Pavithra Ramasamy and Maayan Gazuli. Classification model architecture. Even if sparse models are more parameter efficient, one cannot use pruning to train models that are larger and more accurate than the largest possible dense models. Our code adopts the latest Tensorflow 2 releases, and makes available reference implementations for model training & inference, besides image retrieval and matching functionalities. This pipeline was able to achieve a superior SNP calling accuracy to DeepVariant Illumina on the PrecisionFDA challenge, which is the first time anyone has shown Nanopore outperforming Illumina in this way. Based on this user study, we take a linear combination of the original audio and our produced clean speech channel: output_audio = 0.1 x original_audio + 0.9 x speech. For each of the above visual/auditory attributes, we ran our model on segments from our evaluation set (separate from the training set) and measured the speech enhancement accuracy, broken down according to the different attribute values. Classification model architecture. This latter result was achieved via a combination of more effective neural networks, pooling methods and training protocols (see more details on the Kaggle competition site). Enabling real-time sign language detection in video conferencing is challenging, since applications need to perform classification using the high-volume video feed as the input, which makes the task computationally heavy. In addition, as we scale the learner to a setting with multiple compute engines (e.g., TPU Pod), feeding the data to these engines from a single replay buffer service becomes inefficient, which negatively impacts the overall convergence time. Within the interval considered (i.e., 1x-100x) RigL's performance constantly improves with additional training. Sidhanth Mohanty, University of California, Berkeley, Computational Neuroscience The sequencing process begins with a physical sample being sequenced by any of a handful of instruments, depending on the end goal of the sequencing. The learner trains a model using the sampled data and pushes the updated model back to the actor (e.g. Since YouTube Stories videos are short — limited to 15 seconds — the result of the video processing is available within a couple of seconds after the recording is finished. Similarly, a new large-scale product retrieval competition will capture various challenging aspects, including a very large number of products, a long-tailed class distribution and variations in object appearance and context. For each of the above visual/auditory attributes, we ran our model on segments from our evaluation set (separate from the training set) and measured the speech enhancement accuracy, broken down according to the different attribute values. The learner trains a model using the sampled data and pushes the updated model back to the actor (e.g. Zhengqi Li, Cornell University, Mobile Computing 2019). Case Study: Chip Placement John Cyphert, University of Wisconsin-Madison, Quantum Computing Adding these caching components not only significantly reduces the pressure on the learner to service the read requests, but also further distributes the actors across multiple Borg cells with a marginal communication overhead. By leveraging MediaPipe BlazeFace with GPU accelerated inference, this step is now able to be executed in just a few milliseconds. We congratulate all of this year’s awardees! A Tensorflow implementation of our method along with three other baselines (SET, SNFS, SNIP) can be found at github.com/google-research/rigl. This enables Menger to scale efficiently to thousands of actors across multiple Borg cells. The local feature branch leverages intermediate feature maps to detect salient image regions, with the help of an attention module, and to produce descriptors that represent associated localized contents in a discriminative manner. Overview of an RL system in which an actor sends trajectories (e.g., multiple samples) to a learner. Note that all top methods from that challenge used complex model ensembles, while our results use only a single model.

The number of FLOPs required to train a standard dense ResNet-50 along with its performance is indicated with a dashed red line. Maayan Gazuli, an Israeli Sign Language interpreter, demonstrates the sign language detection system. Earlier state-of-the-art methods could reach ~99.1% accuracy (~73,000 errors) on a 35-fold coverage Illumina whole genome, whereas an early version of DeepVariant (v0.10) had ~99.4% accuracy (46,000 errors), corresponding to a 38% error reduction. In parallel to this new feature in YouTube, we are also exploring additional venues for this technology. Yunusa Simpa Abdulsalm, Mohammed VI Polytechnic University, Programming Technology and Software Engineering

We’d like to thank the co-organizers of the ILR workshop Ondrej Chum, Torsten Sattler, Giorgos Tolias (Czech Technical University), Bohyung Han (Seoul National University), Guangxing Han (Columbia University), Xu Zhang (Amazon), collaborators on the artworks dataset Nanne van Noord, Sarah Ibrahimi (University of Amsterdam), Noa Garcia (Osaka University), as well as our collaborators from the Metropolitan Museum of Art: Jennie Choi, Maria Kessler and Spencer Kiser. Because video conferencing applications usually detect the audio “volume” as talking rather than only detecting speech, this fools the application into thinking the user is speaking. Since the number of text segments is such an important parameter for model performance and compression, it raises the question of whether or not an NLP model needs to be able to distinctly identify every possible text segment. Blog The latest news from Google AI Announcing the 2020 Google PhD Fellows Thursday, October 8, 2020 Posted by Susie Kim, Program Manager, University Relations. There is then a second training stage, fine-tuning, in which the model uses task-specific training data to learn how to use the general pre-trained representations to do a concrete task, like classification. Overview of a distributed RL system with multiple actors placed in different Borg cells with the introduced transparent and distributed caching service. Finally, to avoid processing videos with clean speech (so as to avoid unnecessary computation), we first run our model only on the first two seconds of the video, then compare the speech-enhanced output to the original input audio. This year, we also launched two new challenges within the landmark domain, one focusing on recognition and the other on retrieval. Muratcan Cicek, University of California, Santa Cruz