In general, the overall performance of a neural network depends on several factors. The one usually taking the spotlight is the network architecture, however, this is only one among many important components. An often overlooked contributor to a performant algorithm is the optimizer, which is used to fit the model. Just to illustrate the complexity of optimizing, a ResNet18 architecture has 11689512 parameters. Finding an optimal parameter configuration is locating a point in the 11689512 dimensional space. If we were to brute force this, we might decide to divide this space up to a grid, say we select 10 points along each dimension.

The document analyzer looks for common red flags, probes files if they have components that may have been purposefully obfuscated, and does other checks like examining macros -- the tool in Microsoft Word documents that chains commands together in a series and is often used in attacks. The volume of malicious documents that attackers send out varies widely day to day. Bursztein says that since its deployment, the document scanner has been particularly good at flagging suspicious documents sent in bursts by malicious botnets or through other mass distribution methods. He was also surprised to discover how effective the scanner is at analyzing Microsoft Excel documents, a complicated file format that can be difficult to assess.

We blur the lines of reality with deep learning and next-generation computer graphics. We've created Xpression, a smartphone app which uses deep learning, allowing you to manipulate videos by imprinting your facial movements into a video of another person. Watch the video below to learn about the app. Just how have we done this? Using training data collected by a facial scanning system consisting of 50 cameras and polarized LED lights.

In less than 3 hours, you can understand the theory behind modern artificial intelligence, and apply it with several hands-on examples. This is machine learning on steroids! Find out why everyone's so excited about it and how it really works – and what modern AI can and cannot really do. At the end, you will have a final challenge to create your own deep learning / machine learning system to predict whether real mammogram results are benign or malignant, using your own artificial neural network you have learned to code from scratch with Python. You will need some familiarity with Python and linear algebra to follow along, but if you have that experience, you will find that neural networks are not as complicated as they sound.

Although earlier AI-based models required human supervision and produced inconsistent results, reports STAT, this new deep learning approach was trained on a library of more than 2,000 chemical compounds with something known about their antibacterial potency, using those data to predict function based on structure. The platform identified molecules that looked quite different from existing antibiotics, overcoming the bias that human researchers exhibit when they search for potential anti-bacterial compounds that have structures similar to existing antibiotics, according to STAT.

In the present paper, we propose an extension of the Deep Planning Network (PlaNet), also referred to as PlaNet of the Bayesians (PlaNet-Bayes). There has been a growing demand in model predictive control (MPC) in partially observable environments in which complete information is unavailable because of, for example, lack of expensive sensors. PlaNet is a promising solution to realize such latent MPC, as it is used to train state-space models via model-based reinforcement learning (MBRL) and to conduct planning in the latent space. However, recent state-of-the-art strategies mentioned in MBRR literature, such as involving uncertainty into training and planning, have not been considered, significantly suppressing the training performance. The proposed extension is to make PlaNet uncertainty-aware on the basis of Bayesian inference, in which both model and action uncertainty are incorporated. Uncertainty in latent models is represented using a neural network ensemble to approximately infer model posteriors. The ensemble of optimal action candidates is also employed to capture multimodal uncertainty in the optimality. The concept of the action ensemble relies on a general variational inference MPC (VI-MPC) framework and its instance, probabilistic action ensemble with trajectory sampling (PaETS). In this paper, we extend VI-MPC and PaETS, which have been originally introduced in previous literature, to address partially observable cases. We experimentally compare the performances on continuous control tasks, and conclude that our method can consistently improve the asymptotic performance compared with PlaNet.

Supermarkets need to ensure clean and safe environments for both shoppers and employees. Slips, trips, and falls can result in injuries that have a physical as well as financial cost. Timely detection of hazardous conditions such as spilled liquids or fallen items on supermarket floors can reduce the chances of serious injuries. This paper presents EdgeLite, a novel, lightweight deep learning model for easy deployment and inference on resource-constrained devices. We describe the use of EdgeLite on two edge devices for detecting supermarket floor hazards. On a hazard detection dataset that we developed, EdgeLite, when deployed on edge devices, outperformed six state-of-the-art object detection models in terms of accuracy while having comparable memory usage and inference time.

Being aware of other traffic is a prerequisite for self-driving cars to operate in the real world. In this paper, we show how the intrinsic feature maps of an object detection CNN can be used to uniquely identify vehicles from a dash-cam feed. Feature maps of a pretrained `YOLO' network are used to create 700 deep integrated feature signatures (DIFS) from 20 different images of 35 vehicles from a high resolution dataset and 340 signatures from 20 different images of 17 vehicles of a lower resolution tracking benchmark dataset. The YOLO network was trained to classify general object categories, e.g. classify a detected object as a `car' or `truck'. 5-Fold nearest neighbor (1NN) classification was used on DIFS created from feature maps in the middle layers of the network to correctly identify unique vehicles at a rate of 96.7\% for the high resolution data and with a rate of 86.8\% for the lower resolution data. We conclude that a deep neural detection network trained to distinguish between different classes can be successfully used to identify different instances belonging to the same class, through the creation of deep integrated feature signatures (DIFS).

The use of transfer learning with deep neural networks has increasingly become widespread for deploying well-tested computer vision systems to newer domains, especially those with limited datasets. We describe a transfer learning use case for a domain with a data-starved regime, having fewer than 100 labeled target samples. We evaluate the effectiveness of convolutional feature extraction and fine-tuning of overparameterized models with respect to the size of target training data, as well as their generalization performance on data with covariate shift, or out-of-distribution (OOD) data. Our experiments show that both overparameterization and feature reuse contribute to successful application of transfer learning in training image classifiers in data-starved regimes.

Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while audio-based methods cannot work well in a noisy environment. We instead propose a multimodal pouring network (MP-Net) that is able to robustly predict liquid height by conditioning on both audition and haptics input. MP-Net is trained on a self-collected multimodal pouring dataset. This dataset contains 300 robot pouring recordings with audio and force/torque measurements for three types of target containers. We also augment the audio data by inserting robot noise. We evaluated MP-Net on our collected dataset and a wide variety of robot experiments. Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment. Moreover, we further combine the predicted height and force data to estimate the shape of the target container.