Automatic Speech Recognition (ASR) models need to be optimized for specific hardware before they can be deployed on devices. This can be done by tuning the model’s hyperparameters or exploring variations in its architecture. Re-training and re-validating models after making these changes can be a resource-intensive task. This paper presents TODM (Train Once Deploy Many), a new approach to efficiently train many sizes of hardware-friendly on-device ASR models with comparable GPU-hours to that of a single training job. TODM leverages insights from prior work on Supernet, where Recurrent Neural Network Transducer (RNN-T) models share weights within a Supernet. It reduces layer sizes and widths of the Supernet to obtain subnetworks, making them smaller models suitable for all hardware types. We introduce a novel combination of three techniques to improve the outcomes of the TODM Supernet: adaptive dropout, an in-place Alpha-divergence knowledge distillation, and the use of ScaledAdam optimizer. We validate our approach by comparing Supernet-trained versus individually tuned Multi-Head State Space Model (MH-SSM) RNN-T using LibriSpeech. Results demonstrate that our TODM Supernet either matches or surpasses the performance of manually tuned models by up to a relative of 3% better in word error rate (WER), while efficiently keeping the cost of training many models at a small constant.

Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains.However, the fundamental challenge lies in obtaining high-quality annotations of (utterance, program) pairs across various domains needed for training such models.To overcome this, we propose a novel framework to build a unified multi-domain enabled semantic parser trained only with weak supervision (denotations).Weakly supervised training is particularly arduous as the program search space grows exponentially in a multi-domain setting.To solve this, we incorporate a multipolicy distillation mechanism in which we first train domain-specific semantic parsers (teachers) using weak supervision in the absence of the ground truth programs, followed by training a single unified parser (student) from the domain specific policies obtained from these teachers.The resultant semantic parser is not only compact but also generalizes better, and generates more accurate programs.It further does not require the user to provide a domain label while querying.On the standard OVERNIGHT dataset (containing multiple domains), we demonstrate that the proposed model improves performance by 20% in terms of denotation accuracy in comparison to baseline techniques.

Various projections or views of a heterogeneous information network can be modeled using the graph OLAP (On-line Analytical Processing) framework for effective decision making. Detecting anomalous projections of the network can help the analysts identify regions of interest from the graph specific to the projection attribute. While most previous studies on outlier detection in graphs deal with outlier nodes, edges or subgraphs, we are the first to propose detection of graph cuboid outliers. Further we perform this detection in a query sensitive way. Given a general subgraph query on a heterogeneous network, we study the problem of finding outlier cuboids from the graph OLAP lattice. A Graph Cuboid Outlier (GCOutlier) is a cuboid with exceptionally high density of matches for the query. The GCOutlier detection task is clearly challenging because: (1) finding matches for the query (subgraph isomorphism) is NP-hard; (2) number of matches for the query can be very high; and (3) number of cuboids can be large. We provide an approximate solution to the problem by computing only a fraction of the total matches originating from a select set of candidate nodes and including a select set of edges, chosen smartly. We perform extensive experiments on synthetic datasets to showcase the execution time versus accuracy trade-off. Experiments on real datasets like Four Area and Delicious containing thousands of nodes reveal interesting GCOutliers.