arXiv subjects

Ashish Shrivastava

Publications and source records attributed to Ashish Shrivastava.

23 records · Page 2Linked to original sources

arXiv2020

Towards a Domain Specific Solution for a New Generation of Wireless Modems

Wireless cellular System on Chip (SoC) are experiencing unprecedented demands on data rate, latency use case variety. 5G wireless technologies require a massive number of antennas and complex signal processing to improve bandwidth and spectral efficiency. The Internet of Things is causing a proliferation in the number of connected devices, and service categories, such as ultra-reliable low latency, which will produce new use cases, such as self-driving cars, robotic factories, and remote surgery. In addressing these challenges, we can no longer rely on faster cores, or even more silicon. Modem software development is becoming increasingly error prone and difficult as the complexity of the applications and the architectures increase. In this report we propose a Wireless Domain Specific Solution that takes a Dataflow acceleration approach and addresses the need of the SoC to support dataflows that change with use case and user activity, while maintaining the Firm Real Time High Availability with low probability of Heisenbugs that is required in cellular modems. We do this by developing a Domain Specific Architecture that describes the requirements in a suitably abstracted dataflow Domain Specific language. A toolchain is described that automates translation of those requirements in an efficient and robust manner and provides formal guarantees against Heisenbugs. The dataflow native DSA supports the toolchain output with specialized processing, data management and control features with high performance and low power, and recovers rapidly from dropped dataflows while continuing to achieve the real time requirements. This report focuses on the dataflow acceleration in the DSA and the part of the automated toolchain that formally checks the performance and correctness of software running on this dataflow hardware. Results are presented and a summary of future work is given.

cs.AR↗

arXiv2020

Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis

We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i.e., no style annotation, such as speaker information, is required. Existing unsupervised methods, during training, generate speech by computing style from the corresponding ground truth sample and use a decoder to combine the style vector with the input text. Training the model in such a way leaks content information into the style vector. The decoder can use the leaked content and ignore some of the input text to minimize the reconstruction loss. At inference time, when the reference speech does not match the content input, the output may not contain all of the content of the input text. We refer to this problem as "content leakage", which we address by explicitly estimating and minimizing the mutual information between the style and the content through an adversarial training formulation. We call our method MIST - Mutual Information based Style Content Separation. The main goal of the method is to preserve the input content in the synthesized speech signal, which we measure by the word error rate (WER) and show substantial improvements over state-of-the-art unsupervised speech synthesis methods.

eess.AS↗

arXiv2019

Divide, Denoise, and Defend against Adversarial Attacks

Deep neural networks, although shown to be a successful class of machine learning algorithms, are known to be extremely unstable to adversarial perturbations. Improving the robustness of neural networks against these attacks is important, especially for security-critical applications. To defend against such attacks, we propose dividing the input image into multiple patches, denoising each patch independently, and reconstructing the image, without losing significant image content. We call our method D3. This proposed defense mechanism is non-differentiable which makes it non-trivial for an adversary to apply gradient-based attacks. Moreover, we do not fine-tune the network with adversarial examples, making it more robust against unknown attacks. We present an analysis of the tradeoff between accuracy and robustness against adversarial attacks. We evaluate our method under black-box, grey-box, and white-box settings. On the ImageNet dataset, our method outperforms the state-of-the-art by 19.7% under grey-box setting, and performs comparably under black-box setting. For the white-box setting, the proposed method achieves 34.4% accuracy compared to the 0% reported in the recent works.

cs.CV↗

arXiv2017

Learning from Simulated and Unsupervised Images through Adversarial Training

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simulator. We develop a method for S+U learning that uses an adversarial network similar to Generative Adversarial Networks (GANs), but with synthetic images as inputs instead of random vectors. We make several key modifications to the standard GAN algorithm to preserve annotations, avoid artifacts, and stabilize training: (i) a 'self-regularization' term, (ii) a local adversarial loss, and (iii) updating the discriminator using a history of refined images. We show that this enables generation of highly realistic images, which we demonstrate both qualitatively and with a user study. We quantitatively evaluate the generated images by training models for gaze estimation and hand pose estimation. We show a significant improvement over using synthetic images, and achieve state-of-the-art results on the MPIIGaze dataset without any labeled real data.

cs.CV↗

arXiv2016

Stable Marriage Problem with Ties and Incomplete bounded length preference list under social stability

We consider a variant of socially stable marriage problem where preference lists may be incomplete, may contain ties and may have bounded length. In real world application like NRMP and Scottish medical matching scheme such restrictions arise very frequently where set of agents (man/woman) is very large and providing a complete and strict order preference list is practically in-feasible. In presence of ties in preference lists, the most common solution is weakly socially stable matching. It is a fact that in an instance, weakly stable matching can have different sizes. This motivates the problem of finding a maximum cardinality weakly socially stable matching.

cs.DS↗