Learning-based methodologies increasingly find applications in safety-critical domains like autonomous driving and medical robotics.

However, it is unrealistic to label all the object classes, thus making these supervised learning methods struggle to recognize objects which are unseen during training.

Autonomous part assembly is a challenging yet crucial task in 3D computer vision and robotics. Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation. In this paper, we focus on the pose estimation subproblem from the vision side involving geometric and relational reasoning over the input part geometry. Essentially, the task of generative 3D part assembly is to predict a 6-DoF part pose, including a rigid rotation and translation, for each input part that assembles a single 3D shape as the final output. To tackle this problem, we propose an assembly-oriented dynamic graph learning framework that leverages an iterative graph neural network as a backbone. It explicitly conducts sequential part assembly refinements in a coarse-to-fine manner, exploits a pair of part relation reasoning module and part aggregation module for dynamically adjusting both part features and their relations in the part graph. We conduct extensive experiments and quantitative comparisons to three strong baseline methods, demonstrating the effectiveness of the proposed approach.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper's core finding is that combining an identity classification task as well as metric-learning-style verification task helps to learn better features for face classification/verification. The verification task here tries to decrease feature-space distance between instances of the same identity, and increase distance between those of different identities. This improvement is embedded in a state-of-the-art system for face verification, which uses convnets trained on many (400) different views to generate features, distilled into a small set of 25 using feature selection. Very good results are obtained and experiments performed using LFW as a test set. Overall, these are very good results obtained using a somewhat complex pipeline, and a good investigation into the contribution of each task in the loss for feature learning.

Neural Information Processing Systems http://nips.cc/

In this paper, we introduce the notion of the boundary thickness of a classifier, and we describe its connection with and usefulness for model robustness.

We use adversarial robustness to probe Stringer et al's theory regarding the causal role of a 1/n covariance spectrum. We empirically investigate the benefits such a neural code confers in neural networks, and illuminate its role in multi-layer architectures.

Neural Information Processing Systems http://nips.cc/

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The submission describes a convex deep learning formulation that leverages a number of key ideas. First, a training objective is proposed that explicitly includes the outputs of hidden layers as variables to be inferred via optimization. These are linked to linear responses via a loss function, and the net objective is the sum of these loss functions across the layers, plus some regularization terms. Next, a number of changes of variables are performed in order to reparameterize the objective into a convex form, heavily leveraging the representer theorem and the idea of value regularization. We are left with a convex objective in terms of three different matrices (per layer) to optimize. In particular, one of these matrices is a nonparametric'normalized output kernel' matrix, which takes the place of optimizing over the hidden layer outputs directly; however, this leads to a transductive method where we must simultaneously solve the optimization for training and test inputs.