VeriSparse: Training Verified Locally Robust Sparse Neural Networks from Scratch

Several safety-critical applications such as self-navigation, health care, and industrial control systems use embedded systems as their core. Recent advancements in Neural Networks (NNs) in approximating complex functions make them well-suited for these domains. However, the compute-intensive nature of NNs limits their deployment and training in embedded systems with limited computation and storage capacities. Moreover, the adversarial vulnerability of NNs challenges their use in safety-critical scenarios. Hence, developing sparse models having robustness guarantees while leveraging fewer resources during training is critical in expanding NNs' use in safety-critical and resource-constrained embedding system settings. This paper presents 'VeriSparse'-- a framework to search verified locally robust sparse networks starting from a random sparse initialization (i.e., scratch). VeriSparse obtains sparse NNs exhibiting similar or higher verified local robustness, requiring one-third of the training time compared to the state-of-the-art approaches. Furthermore, VeriSparse performs both structured and unstructured sparsification, enabling storage, computing-resource, and computation time reduction during inference generation. Thus, it facilitates the resource-constraint embedding platforms to leverage verified robust NN models, expanding their scope to safety-critical, real-time, and edge applications. We exhaustively investigated VeriSparse's efficacy and generalizability by evaluating various benchmark and application-specific datasets across several model architectures.Comment: 21 pages, 13 tables, 3 figure

Sparsely Aggregated Convolutional Networks

We explore a key architectural aspect of deep convolutional neural networks: the pattern of internal skip connections used to aggregate outputs of earlier layers for consumption by deeper layers. Such aggregation is critical to facilitate training of very deep networks in an end-to-end manner. This is a primary reason for the widespread adoption of residual networks, which aggregate outputs via cumulative summation. While subsequent works investigate alternative aggregation operations (e.g. concatenation), we focus on an orthogonal question: which outputs to aggregate at a particular point in the network. We propose a new internal connection structure which aggregates only a sparse set of previous outputs at any given depth. Our experiments demonstrate this simple design change offers superior performance with fewer parameters and lower computational requirements. Moreover, we show that sparse aggregation allows networks to scale more robustly to 1000+ layers, thereby opening future avenues for training long-running visual processes.Comment: Accepted to ECCV 201

Neural Ranking Models with Weak Supervision

Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from queries and documents when no supervised signal is available. Hence, in this paper, we propose to train a neural ranking model using weak supervision, where labels are obtained automatically without human annotators or any external resources (e.g., click data). To this aim, we use the output of an unsupervised ranking model, such as BM25, as a weak supervision signal. We further train a set of simple yet effective ranking models based on feed-forward neural networks. We study their effectiveness under various learning scenarios (point-wise and pair-wise models) and using different input representations (i.e., from encoding query-document pairs into dense/sparse vectors to using word embedding representation). We train our networks using tens of millions of training instances and evaluate it on two standard collections: a homogeneous news collection(Robust) and a heterogeneous large-scale web collection (ClueWeb). Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections. Our findings also suggest that supervised neural ranking models can greatly benefit from pre-training on large amounts of weakly labeled data that can be easily obtained from unsupervised IR models.Comment: In proceedings of The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2017