no code implementations • 24 Jun 2022 • Pranay Manocha, Anurag Kumar, Buye Xu, Anjali Menon, Israel D. Gebru, Vamsi K. Ithapu, Paul Calamia
Audio quality assessment is critical for assessing the perceptual realism of sounds.
1 code implementation • 19 Oct 2021 • Efthymios Tzinis, Yossi Adi, Vamsi K. Ithapu, Buye Xu, Anurag Kumar
Specifically, a separation teacher model is pre-trained on an out-of-domain dataset and is used to infer estimated target signals for a batch of in-domain mixtures.
no code implementations • 29 May 2021 • Pranay Manocha, Anurag Kumar, Buye Xu, Anjali Menon, Israel D. Gebru, Vamsi K. Ithapu, Paul Calamia
Subjective evaluations are critical for assessing the perceptual realism of sounds in audio-synthesis driven technologies like augmented and virtual reality.
1 code implementation • ICML 2017 • Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba, Vikas Singh
Many studies in biomedical and health sciences involve small sample sizes due to logistic or financial constraints.
no code implementations • CVPR 2017 • Vamsi K. Ithapu, Risi Kondor, Sterling C. Johnson, Vikas Singh
Multiresolution analysis and matrix factorization are foundational tools in computer vision.
no code implementations • 4 Mar 2017 • Felipe Gutierrez-Barragan, Vamsi K. Ithapu, Chris Hinrichs, Camille Maumet, Sterling C. Johnson, Thomas E. Nichols, Vikas Singh, the ADNI
We find that RapidPT achieves its best runtime performance on medium sized datasets ($50 \leq n \leq 200$), with speedups of 1. 5x - 38x (vs. SnPM13) and 20x-1000x (vs. NaivePT).
no code implementations • 28 Feb 2017 • Vamsi K. Ithapu, Sathya N. Ravi, Vikas Singh
We seek to analyze whether network architecture and input data statistics may guide the choices of learning parameters and vice versa.
no code implementations • NeurIPS 2016 • Hao Zhou, Vamsi K. Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, Sterling C. Johnson
Consider samples from two different data sources $\{\mathbf{x_s^i}\} \sim P_{\rm source}$ and $\{\mathbf{x_t^i}\} \sim P_{\rm target}$.
no code implementations • ICCV 2015 • Lopamudra Mukherjee, Sathya N. Ravi, Vamsi K. Ithapu, Tyler Holmes, Vikas Singh
In this paper, we first derive an Augmented Lagrangian approach to optimize the standard binary Hashing objective (i. e., maintain fidelity with a given distance matrix).
no code implementations • ICCV 2015 • Seong Jae Hwang, Maxwell D. Collins, Sathya N. Ravi, Vamsi K. Ithapu, Nagesh Adluru, Sterling C. Johnson, Vikas Singh
Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation.
no code implementations • 17 Nov 2015 • Vamsi K. Ithapu, Sathya N. Ravi, Vikas Singh
The regularization and output consistency behavior of dropout and layer-wise pretraining for learning deep networks have been fairly well studied.
no code implementations • 10 Jun 2015 • Vamsi K. Ithapu, Sathya Ravi, Vikas Singh
Unsupervised pretraining and dropout have been well studied, especially with respect to regularization and output consistency.
no code implementations • NeurIPS 2013 • Chris Hinrichs, Vamsi K. Ithapu, Qinyuan Sun, Sterling C. Johnson, Vikas Singh
In this paper, we show that permutation testing in fact amounts to populating the columns of a very large matrix ${\bf P}$.
no code implementations • 12 Feb 2015 • Vamsi K. Ithapu, Sathya Ravi, Vikas Singh
The success of deep architectures is at least in part attributed to the layer-by-layer unsupervised pre-training that initializes the network.