Exploring the Lottery Ticket Hypothesis

Pruning is a well known Machine Learning technique in which unnecessary weights are removed from a neural network model after training. In some cases, pruning can reduce model sizes by more than 90% without compromising on model accuracy while potentially offering a significant reduction in inference memory usage (see some great examples here). In 2018, […]

Attention Augmented Convolutional Networks

Convolutional neural networks have proven to be a powerful tool for image recognition, allowing for ever-improving results in image classification (ImageNet), object detection (COCO), and other tasks. Despite their success, convolutions are limited by their locality, i.e. their inability to consider relations between different areas of an image. On the other hand, a popular mechanism […]

TossingBot – Teaching Robots to Throw Objects Accurately

One of the most well-known challenges in robotics is ‘picking’, i.e. using a robotic claw to lift a single object, usually from a cluttered 3-dimensional pile of objects. Picking an object from a pile and moving it to a destination can be a useful capability in many real-world situations but human experience shows that in […]

BagNet – Solving ImageNet with a Simple Bag-of-features Model

Prior to 2012, most machine learning algorithms were statistical models which used hand-created features. The models were highly explainable and somewhat effective but failed to reach a high accuracy in many language and computer vision tasks. In 2012, AlexNet, a deep neural network model, won the 2012 ImageNet competition by a large margin, and ignited […]

Identifying and Correcting Label Bias in Machine Learning

As machine learning (ML) becomes more effective and widespread it is becoming more prevalent in systems with real-life impact, from loan recommendations to job application decisions. With the growing usage comes the risk of bias – biased training data could lead to biased ML algorithms, which in turn could perpetuate discrimination and bias in society. […]

Sim2Real – Using Simulation to Train Real-Life Grasping Robots

Grasping real-world objects is considered one of the more iconic examples of the current limits of machine intelligence. While humans can easily grasp and pick up objects they’ve never seen before, even the most advanced robotic arms can’t manipulate objects that they weren’t trained to handle. Recent developments in reinforcement learning (RL) have allowed for […]

SlowFast – Dual-mode CNN for Video Understanding

Detecting objects in images and categorizing them is one of the more well-known Computer Vision tasks, popularized by the 2010 ImageNet dataset and challenge. While much progress has been achieved on ImageNet, a still vexing task is video understanding – analyzing a video segment and explaining what’s happening inside of it. Despite some recent progress […]

Struct2Depth – Predicting object depth in dynamic environments

While recent advances in computer vision are helping robots and autonomous vehicles navigate complex environments effectively, some challenges still remain. One major challenge is depth prediction, i.e. the ability of a moving robot to recognize the depth of objects around it, a requirement for it to navigate a real-life environment safely. Historically, the most effective […]

GPipe – Training Giant Neural Nets using Pipeline Parallelism

In recent years the size of machine learning datasets and models has been constantly increasing, allowing for improved results on a wide range of tasks. At the same time hardware acceleration (GPUs, TPUs) has also been improving but at a significantly slower pace. The gap between model growth and hardware improvement has increased the importance […]

Advancing to 3D Deep Neural Networks in Medical Image Analysis

For several decades computer scientists have been attempting to build medical software to help physicians analyze medical images. Until 2012, when deep neural networks first proved their effectiveness, most attempts included extensive feature engineering tailored to specific types of medical images, and were usually low-quality and therefore ineffective in helping doctors in practice. In recent […]