no code implementations • 17 Mar 2024 • Xue Bai, Tasmiah Haque, Sumit Mohan, Yuliang Cai, Byungheon Jeong, Adam Halasz, Srinjoy Das
Prediction of keypoints, to enable transmission using lower frames per second on the source device, is performed using a Variational Recurrent Neural Network (VRNN).
no code implementations • 1 Mar 2024 • Hamed Khosravi, Srinjoy Das, Abdullah Al-Mamun, Imtiaz Ahmed
Early prediction of dialysis is crucial as it can significantly improve patient outcomes and assist healthcare providers in making timely and informed decisions.
no code implementations • 7 Dec 2023 • Israt Zarin Era, Imtiaz Ahmed, Zhichao Liu, Srinjoy Das
Foundation models are currently driving a paradigm shift in computer vision tasks for various fields including biology, astronomy, and robotics among others, leveraging user-generated prompts to enhance their performance.
no code implementations • 16 Nov 2023 • Ahmed Shoyeb Raihan, Hamed Khosravi, Srinjoy Das, Imtiaz Ahmed
The UCB-to-EI switching policy dictated guided through continuous monitoring of the model uncertainty during each step of sequential sampling results in navigating through the MDS more efficiently while ensuring rapid convergence.
no code implementations • 15 Nov 2023 • Hamed Khosravi, Sarah Farhadpour, Manikanta Grandhi, Ahmed Shoyeb Raihan, Srinjoy Das, Imtiaz Ahmed
In this article, operational data is analyzed from a paper manufacturing machine in which paper breaks are relatively rare but have a high economic impact.
no code implementations • 22 Aug 2023 • Srinjoy Das, Lawrence Rauchwerger
Especially in the case of inference, when the batch size is 1 and execution is on CPUs or for power-constrained edge devices, current techniques can become costly, complicated or inapplicable.
2 code implementations • 15 Oct 2021 • Xinyu Zhang, Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das
State-of-the-art quantization techniques are currently applied to both the weights and activations; however, pruning is most often applied to only the weights of the network.
no code implementations • 6 Oct 2021 • Xinyu Zhang, Srinjoy Das, Ken Kreutz-Delgado
We propose a novel modification of the standard upper confidence bound (UCB) method for the stochastic multi-armed bandit (MAB) problem which tunes the confidence bound of a given bandit based on its distance to others.
no code implementations • 29 Sep 2021 • Srinjoy Das, Hrushikesh Mhaskar, Alexander Cloninger
Applications are demonstrated for clustering of synthetic and real-life time series and image data, and the performance of kdiff is compared to competing distance measures for clustering.
1 code implementation • 15 Jul 2021 • Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das
We analyze and compare the inference properties of convolution-based upsampling algorithms using a quantitative model of incurred time and energy costs and show that using deconvolution for inference at the edge improves both system latency and energy efficiency when compared to their sub-pixel or resize convolution counterparts.
no code implementations • 31 Jan 2021 • Siqiao Ruan, Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das
The use of Deep Learning hardware algorithms for embedded applications is characterized by challenges such as constraints on device power consumption, availability of labeled data, and limited internet bandwidth for frequent training on cloud servers.
no code implementations • 28 Oct 2019 • Alexander Potapov, Ian Colbert, Ken Kreutz-Delgado, Alexander Cloninger, Srinjoy Das
Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, image occlusion removal, pattern completion, and motion synthesis.
no code implementations • 11 Mar 2019 • Ian Colbert, Ken Kreutz-Delgado, Srinjoy Das
The power budget for embedded hardware implementations of Deep Learning algorithms can be extremely tight.
1 code implementation • 7 May 2017 • Xin-Yu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado
In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors.
no code implementations • 13 Apr 2017 • Xiaojing Xu, Srinjoy Das, Ken Kreutz-Delgado
Probabilistic generative neural networks are useful for many applications, such as image classification, speech recognition and occlusion removal.
no code implementations • 18 Feb 2016 • Ojash Neopane, Srinjoy Das, Ery Arias-Castro, Kenneth Kreutz-Delgado
Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in probabilistic generative model applications such as image occlusion removal, pattern completion and motion synthesis.
no code implementations • 24 Sep 2015 • Bruno U. Pedroni, Srinjoy Das, John V. Arthur, Paul A. Merolla, Bryan L. Jackson, Dharmendra S. Modha, Kenneth Kreutz-Delgado, Gert Cauwenberghs
For this, we first propose a method of producing the Gibbs sampler using bio-inspired digital noisy integrate-and-fire neurons.
no code implementations • 26 Mar 2015 • Srinjoy Das, Bruno Umbria Pedroni, Paul Merolla, John Arthur, Andrew S. Cassidy, Bryan L. Jackson, Dharmendra Modha, Gert Cauwenberghs, Ken Kreutz-Delgado
Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition.
no code implementations • 5 Nov 2013 • Emre Neftci, Srinjoy Das, Bruno Pedroni, Kenneth Kreutz-Delgado, Gert Cauwenberghs
However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate.