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A central challenge in sensory neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayered neural circuits, nonlinear processes such as synaptic transmission and spiking dynamics present a significant obstacle to the creation of accurate computational models… (More)

- Aran Nayebi, Surya Ganguli
- ArXiv
- 2017

Inspired by biophysical principles underlying nonlinear dendritic computation in neural circuits, we develop a scheme to train deep neural networks to make them robust to adversarial attacks. Our scheme generates highly nonlinear, saturated neural networks that achieve state of the art performance on gradient based adversarial examples on MNIST, despite… (More)

- Aran Nayebi, Matt Vitelli
- 2015

We compare the performance of two different types of recurrent neural networks (RNNs) for the task of algorithmic music generation, with audio waveforms as input. In particular, we focus on RNNs that have a sophisticated gating mechanism, namely, the Long Short-Term Memory (LSTM) network and the recently introduced Gated Recurrent Unit (GRU). Our results… (More)

- Aran Nayebi
- ArXiv
- 2012

- Aran Nayebi
- 2009

Abstract. On distributed memory electronic computers, the implementation and association of fast parallel matrix multiplication algorithms has yielded astounding results and insights. In this discourse, we use the tools of molecular biology to demonstrate the theoretical encoding of Strassen’s fast matrix multiplication algorithm with DNA based on an… (More)

We leverage vector space embeddings of sentences and nearest-neighbor methods to transform a small amount of labelled training data into a significantly larger training set using an unlabelled corpus. The quality of the larger training set is measured by prediction accuracy on a benchmark sentiment analysis task. Our results indicate it is possible to… (More)

- Matt Vitelli, Aran Nayebi
- 2016

We created a deep Q-network (DQN) agent to perform the task of autonomous car driving from raw sensory inputs. We evaluated our agent’s performance against several standard agents in a racing simulation environment. Our results indicate that our DQN agent is capable of successfully controlling a car to navigate around a simulation environment.

- Aran Nayebi
- Minds and Machines
- 2013

For over a decade, the hypercomputation movement has produced computational models that in theory solve the algorithmically unsolvable, but they are not physically realizable according to currently accepted physical theories. While opponents to the hypercomputation movement provide arguments against the physical realizability of specific models in order to… (More)

- Pradeep Javangula, Kourosh Modarre, Paresh Shenoy, Yi Liu, Aran Nayebi
- ICCS
- 2017

- Aran Nayebi, Virginia Vassilevska Williams
- ArXiv
- 2014

We consider the quantum time complexity of the all pairs shortest paths (APSP) problem and some of its variants. The trivial classical algorithm for APSP and most all pairs path problems runs in O(n) time, while the trivial algorithm in the quantum setting runs in Õ(n) time, using Grover search. A major open problem in classical algorithms is to obtain a… (More)