#### Filter Results:

- Full text PDF available (13)

#### Publication Year

2011

2017

- This year (1)
- Last 5 years (11)
- Last 10 years (12)

#### Publication Type

#### Co-author

#### Publication Venue

#### Key Phrases

Learn More

- Mariusz Bojarski, Davide Del Testa, +10 authors Karol Zieba
- ArXiv
- 2016

We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with… (More)

- Cijo Jose, Prasoon Goyal, Parv Aggrwal, Manik Varma
- ICML
- 2013

Our objective is to speed up non-linear SVM prediction while maintaining classification accuracy above an acceptable limit. We generalize Localized Multiple Kernel Learning so as to learn a tree-based primal feature embedding which is high dimensional and sparse. Primal based classification decouples prediction costs from the number of support vectors and… (More)

- Happy Mittal, Prasoon Goyal, Vibhav Gogate, Parag Singla
- NIPS
- 2014

Lifted inference algorithms for probabilistic first-order logic frameworks such as Markov logic networks (MLNs) have received significant attention in recent years. These algorithms use so called lifting rules to identify symmetries in the first-order representation and reduce the inference problem over a large probabilistic model to an inference problem… (More)

- Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric P. Xing
- ArXiv
- 2017

The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich repre-sentational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution , thereby restricting its applications to relatively simple… (More)

- Corinna Cortes, Prasoon Goyal, Vitaly Kuznetsov, Mehryar Mohri
- FE@NIPS
- 2015

This paper studies a new framework for learning a predictor in the presence of multiple kernel functions where the learner selects or extracts several kernel functions from potentially complex families and finds an accurate predictor defined in terms of these functions. We present an algorithm , Voted Kernel Regularization, that provides the flexibility of… (More)

Optimization problems are ubiquitous in machine learning, while MATLAB provides excellent implementations of various most commonly used optimization algorithms. In this work, we comapre the MATLAB implementations of various algorithms for optimization problems in machine learning.

- Prasoon Goyal
- 2011

— This paper gives a glimpse of basic quantum computing concepts, which is an active area of research in today's world. The discussion starts with difference between a quantum computer and a classical one at the bit level, register level, logic gates level and at circuit level. Then, some of the important quantum phenomena such as quantum entanglement and… (More)

Disclaimer: This report is submitted to NYU's Computer Science Department for the sole purpose of assigning an internship grade. The information remains confidential and proprietary to the company. Abstract We consider the problem of captioning videos in the wild using deep learning techniques. The aim was to improve over the existing state-of-the-art… (More)

- Prasoon Goyal, Sahil Goel
- 2015

We propose a novel GPU variant for genetic algorithm that uses constant memory effectively to store and share the elite population across different blocks to obtain faster convergence rates. We compare our algorithm to a previous work which does not use constant memory, and show that using constant memory significantly improves the quality of solution for a… (More)

Here the first two formulas are as considered earlier in the main text. We have also added another formula R(W) to the theory. Let the domain of each of the variables be ∆ = {a, b, c}. This theory consists of 3 equivalence classes given by E