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- 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 representational 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 phenomena.… (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)

- Prashanth Shetty, Shweta Hegde, K S Vinod, Salona Kalra, Prasoon Goyal, Mimansha Patel
- The journal of contemporary dental practice
- 2016

INTRODUCTION
A case control study was carried out to evaluate the synergistic effects of habits quantified by habit index and its effect and severity on the clinicopathological features of oral leukoplakia in a cohort of 100 patients visiting Triveni Dental College, Bilaspur, Chhattisgarh, India.
MATERIALS AND METHODS
One hundred patients indulging in… (More)

- Lars Hellsten, Brian Roark, +5 authors David Rybach
- FSMNLP
- 2017

We present an extension to a mobile keyboard input decoder based on finite-state transducers that provides general transliteration support, and demonstrate its use for input of South Asian languages using a QWERTY keyboard. On-device keyboard decoders must operate under strict latency and memory constraints, and we present several transducer optimizations… (More)

This paper provides a glimpse of basic probabilistic database concepts, which is an active area of research in today’s world. The discussion starts with the need for probabilistic databases, and their advantages over conventional databases in certain circumstances. Then, some of the key aspects of probabilistic databases are discussed, which include topics… (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 E1 = {X,Z,U} and E2 = {Y, V } and E3 = {W}. Here, E2 and E3 are single occurrence classes whereas E1 is not. Let us… (More)

- Corinna Cortes, Prasoon Goyal, Vitaly Kuznetsov, Mehryar Mohri
- ArXiv
- 2015

This paper presents an algorithm, Voted Kernel Regularization , that provides the flexibility of using potentially very complex kernel functions such as predictors based on much higher-degree polynomial kernels, while benefitting from strong learning guarantees. The success of our algorithm arises from derived bounds that suggest a new regularization… (More)