Blackbox End-to-End Verification of Ground Robot Safety and Liveness

We formally prove end-to-end correctness of a ground robot implemented in a simulator. We use an untrusted controller supervised by a verified sandbox. Contributions include: (i) A model of the robot in differential dynamic logic, which specifies assumptions on the controller and robot kinematics, (ii) Formal proofs of safety and liveness for a waypoint-following problem with speed limits, (iii) An automatically synthesized sandbox, which is automatically proven to enforce model compliance at runtime, and (iv) Controllers, planners, and environments for the simulations. The verified sandbox is used to safeguard (unverified) controllers in a realistic simulated environment. Experimental evaluation of the resulting sandboxed implementation confirms safety and high model-compliance, with an inherent trade-off between compliance and performance. The verified sandbox thus serves as a valuable bidirectional link between formal methods and implementation, automating both enforcement of safety and model validation simultaneously.

Improved Recurrent Neural Networks for Session-based Recommendations

Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively.

Multi-Modal Hybrid Deep Neural Network for Speech Enhancement

Deep Neural Networks (DNN) have been successful in en- hancing noisy speech signals. Enhancement is achieved by learning a nonlinear mapping function from the features of the corrupted speech signal to that of the reference clean speech signal. The quality of predicted features can be improved by providing additional side channel information that is robust to noise, such as visual cues. In this paper we propose a novel deep learning model inspired by insights from human audio visual perception. In the proposed unified hybrid architecture, features from a Convolution Neural Network (CNN) that processes the visual cues and features from a fully connected DNN that processes the audio signal are integrated using a Bidirectional Long Short-Term Memory (BiLSTM) network. The parameters of the hybrid model are jointly learned using backpropagation. We compare the quality of enhanced speech from the hybrid models with those from traditional DNN and BiLSTM models.