Beyond Short Snippets: Deep Networks for Video Classification

Convolutional neural networks (CNNs) have been extensively applied for image recognition problems giving state-of-the-art results on recognition, detection, segmentation and retrieval. In this work we propose and evaluate several deep neural network architectures to combine image information across a video over longer time periods than previously attempted. We propose two methods capable of handling full length videos. The first method explores various convolutional temporal feature pooling architectures, examining the various design choices which need to be made when adapting a CNN for this task. The second proposed method explicitly models the video as an ordered sequence of frames. For this purpose we employ a recurrent neural network that uses Long Short-Term Memory (LSTM) cells which are connected to the output of the underlying CNN. Our best networks exhibit significant performance improvements over previously published results on the Sports 1 million dataset (73.1% vs. 60.9%) and the UCF-101 datasets with (88.6% vs. 88.0%) and without additional optical flow information (82.6% vs. 72.8%)

Building high-level features using large scale unsupervised learning

We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting with these learned features, we trained our network to obtain 15.8% accuracy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art

Dynamic Control Flow in Large-Scale Machine Learning

Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments. This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations. We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability.Comment: Appeared in EuroSys 2018. 14 pages, 16 figure

MLSys: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two

SKOPE-Study of Ketorolac vs Opioid for Pain after Endoscopy: A Double-Blinded Randomized Control Trial in Patients Undergoing Ureteroscopy

PURPOSE: Pain is the leading cause of unplanned emergency department visits and readmissions after ureteroscopy, making postoperative analgesic stewardship a priority given the current opioid epidemic. We conducted a double-blinded, randomized controlled trial, with noninferiority design, comparing nonsteroidal anti-inflammatory drugs to opiates for postoperative pain control in patients undergoing ureteroscopy for urolithiasis. MATERIALS AND METHODS: Patients were randomized and blinded to either oxycodone (5 mg) or ketorolac (10 mg), taken as needed, with 3 nonblinded oxycodone rescue pills for breakthrough pain. Primary study outcome was visual analogue scale pain score on postoperative days 1-5. Secondary outcomes included medication utilization, side effects, and Ureteral Stent Symptom Questionnaire scores. RESULTS: Eighty-one patients were included (43 oxycodone, 38 ketorolac). The two groups had comparable patient, stone, and perioperative characteristics. No differences were found in post-operative pain scores, study medication or rescue pill usage, or side effects. Higher maximum pain scores on days 1-5 (p\u3c0.05) and higher USSQ score (28.1 vs 21.7, p=0.045) correlated with analgesic usage, irrespective of treatment group. Patients receiving ketorolac reported significantly fewer days confined to bed (1.3±1.3 vs 2.3±2.6, p=0.02). There was no difference in unscheduled post-operative physician encounters. CONCLUSIONS: This is the first double-blinded randomized controlled trial comparing nonsteroidal anti-inflammatory drugs and opiates post-ureteroscopy, and demonstrates noninferiority of nonsteroidal anti-inflammatory drugs in pain control with similar efficacy, safety profile, physician contact and notably, earlier convalescence compared to the opioid group. This provides strong evidence against routine opioid use post-ureteroscopy, justifying continued investigation into reducing postoperative opiate prescriptions