#### Facebook at ICCV 2017

Computer vision experts from around the world will gather in Venice this week at the International Conference on Computer Vision […] Facebook at ICCV 2017 by Kelly Berschauer

#### Recent Advances in Convolutional Neural Networks

Convolutional Neural Network DNN image language speech

In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Leveraging…

#### Photo-Guided Exploration of Volume Data Features

In this work, we pose the question of whether, by considering qualitative information such as a sample target image as input, one can produce a rendered image of scientific data that is similar to the target. The algorithm resulting from our research allows one to ask the question of whether featur…

#### Learning Social Image Embedding with Deep Multimodal Attention Networks

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text …

#### Infinite-Label Learning with Semantic Output Codes

We develop a new statistical machine learning paradigm, named infinite-label learning, to annotate a data point with more than one relevant labels from a candidate set, which pools both the finite labels observed at training and a potentially infinite number of previously unseen labels. The infinit…

#### Multi-task Domain Adaptation for Deep Learning of Instance Grasping from Simulation

Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi-task domain adaptation framework for instance grasping in cluttered scenes by utilizing simulated robot experiments. Our neural network …

#### Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models

This paper presents the results and conclusions of our participation in the Clickbait Challenge 2017 on automatic clickbait detection in social media. We first describe linguistically-infused neural network models and identify informative representations to predict the level of clickbaiting present…

#### A Convex Similarity Index for Sparse Recovery of Missing Image Samples

This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted especially for image signals. Instead of $l_2$-norm or Mean Square Error (MSE), a new perceptual quality measure is used as the similarity criterion between the original and the re…

#### Low-shot learning with large-scale diffusion

Convolutional Neural Network image

This paper considers the problem of inferring image labels for which only a few labelled examples are available at training time. This setup is often referred to as low-shot learning in the literature, where a standard approach is to re-train the last few layers of a convolutional neural network le…

#### Learning to Transfer Initializations for Bayesian Hyperparameter Optimization

Hyperparameter optimization undergoes extensive evaluations of validation errors in order to find the best configuration of hyperparameters. Bayesian optimization is now popular for hyperparameter optimization, since it reduces the number of validation error evaluations required. Suppose that we ar…

#### Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks

An important goal of computer vision is to build systems that learn visual representations over time that can be applied to many tasks. In this paper, we investigate a vision-language embedding as a core representation and show that it leads to better cross-task transfer than standard multi-task le…

#### Vector Quantization using the Improved Differential Evolution Algorithm for Image Compression

Vector Quantization, VQ is a popular image compression technique with a simple decoding architecture and high compression ratio. Codebook designing is the most essential part in Vector Quantization. LindeBuzoGray, LBG is a traditional method of generation of VQ Codebook which results in lower PSNR …

#### A graphical, scalable and intuitive method for the placement and the connection of biological cells

We introduce a graphical method originating from the computer graphics domain that is used for the arbitrary and intuitive placement of cells over a two-dimensional manifold. Using a bitmap image as input, where the color indicates the identity of the different structures and the alpha channel indi…

#### Learning to Generalize: Meta-Learning for Domain Generalization

Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. {Domain Generalization} (DG) techniques attempt to alleviate this issue by producing models which by design generalize well to novel tes…

#### Energy-efficient Amortized Inference with Cascaded Deep Classifiers

Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing. We address this problem by proposing a novel framework that optimizes the prediction accuracy and energy cost simultan…

#### Standard detectors aren’t (currently) fooled by physical adversarial stop signs

An adversarial example is an example that has been adjusted to produce the wrong label when presented to a system at test time. If adversarial examples existed that could fool a detector, they could be used to (for example) wreak havoc on roads populated with smart vehicles. Recently, we described …

#### Checkpoint Ensembles: Ensemble Methods from a Single Training Process

Convolutional Neural Network health image text

We present the checkpoint ensembles method that can learn ensemble models on a single training process. Although checkpoint ensembles can be applied to any parametric iterative learning technique, here we focus on neural networks. Neural networks’ composable and simple neurons make it possibl…

#### On denoising autoencoders trained to minimise binary cross-entropy

Denoising autoencoders (DAEs) are powerful deep learning models used for feature extraction, data generation and network pre-training. DAEs consist of an encoder and decoder which may be trained simultaneously to minimise a loss (function) between an input and the reconstruction of a corrupted vers…

#### Protein identification with deep learning: from abc to xyz

Convolutional Neural Network DNN health image RNN

Proteins are the main workhorses of biological functions in a cell, a tissue, or an organism. Identification and quantification of proteins in a given sample, e.g. a cell type under normal/disease conditions, are fundamental tasks for the understanding of human health and disease. In this paper, we…

#### Texture Fuzzy Segmentation using Skew Divergence Adaptive Affinity Functions

Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been successfully used in the segmentation of images from a wide variety of sources. However, the traditional fuzzy segmentation algorithm fails t…

#### End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design

We develop an end-to-end training algorithm for whole-image breast cancer diagnosis based on mammograms. It requires lesion annotations only at the first stage of training. After that, a whole image classifier can be trained using only image level labels. This greatly reduced the reliance on lesion…

#### Projection Based Weight Normalization for Deep Neural Networks

CIFAR Convolutional Neural Network DNN gradient image IMAGENET MNIST

Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-n…

#### How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?

Convolutional Neural Network DNN image

In the last few years, we have seen the rise of deep learning applications in a broad range of chemistry research problems. Recently, we reported on the development of Chemception, a deep convolutional neural network (CNN) architecture for general-purpose small molecule property prediction. In this…

#### Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification

Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples. TDA, thus, yields key shape descriptors in the form of persistent topological features that can be used for any supervised or …

#### Machine Learning in Appearance-based Robot Self-localization

An appearance-based robot self-localization problem is considered in the machine learning framework. The appearance space is composed of all possible images, which can be captured by a robot’s visual system under all robot localizations. Using recent manifold learning and deep learning techni…