Author name cluster

Rajesh Rao

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

19 papers

1 author row

NeurIPS Conference 2019 Conference Paper

A Bayesian Theory of Conformity in Collective Decision Making

Koosha Khalvati
Saghar Mirbagheri
Seongmin Park
Jean-Claude Dreher
Rajesh Rao

In collective decision making, members of a group need to coordinate their actions in order to achieve a desirable outcome. When there is no direct communication between group members, one should decide based on inferring others' intentions from their actions. The inference of others' intentions is called "theory of mind" and can involve different levels of reasoning, from a single inference on a hidden variable to considering others partially or fully optimal and reasoning about their actions conditioned on one's own actions (levels of “theory of mind”). In this paper, we present a new Bayesian theory of collective decision making based on a simple yet most commonly observed behavior: conformity. We show that such a Bayesian framework allows one to achieve any level of theory of mind in collective decision making. The viability of our framework is demonstrated on two different experiments, a consensus task with 120 subjects and a volunteer's dilemma task with 29 subjects, each with multiple conditions.

AAAI Conference 2018 Conference Paper

AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video

Nancy Wang
Ali Farhadi
Rajesh Rao
Bingni Brunton

Developing useful interfaces between brains and machines is a grand challenge of neuroengineering. An effective interface has the capacity to not only interpret neural signals, but predict the intentions of the human to perform an action in the near future; prediction is made even more challenging outside well-controlled laboratory experiments. This paper describes our approach to detect and to predict natural human arm movements in the future, a key challenge in brain computer interfacing that has never before been attempted. We introduce the novel Annotated Joints in Long-term ECoG (AJILE) dataset; AJILE includes automatically annotated poses of 7 upper body joints for four human subjects over 670 total hours (more than 72 million frames), along with the corresponding simultaneously acquired intracranial neural recordings. The size and scope of AJILE greatly exceeds all previous datasets with movements and electrocorticography (ECoG), making it possible to take a deep learning approach to movement prediction. We propose a multimodal model that combines deep convolutional neural networks (CNN) with long short-term memory (LSTM) blocks, leveraging both ECoG and video modalities. We demonstrate that our models are able to detect movements and predict future movements up to 800 msec before movement initiation. Further, our multimodal movement prediction models exhibit resilience to simulated ablation of input neural signals. We believe a multimodal approach to natural neural decoding that takes context into account is critical in advancing bioelectronic technologies and human neuroscience.

AAAI Conference 2018 Conference Paper

Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps

Kaiyu Zheng
Andrzej Pronobis
Rajesh Rao

We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expressed in terms of arbitrary, dynamic graphs. While many approaches to structured prediction place strict constraints on the interactions between inferred variables, many real-world problems can be only characterized using complex graph structures of varying size, often contaminated with noise when obtained from real data. Here, we focus on one such problem in the domain of robotics. We demonstrate how GraphSPNs can be used to bolster inference about semantic, conceptual place descriptions using noisy topological relations discovered by a robot exploring large-scale ofﬁce spaces. Through experiments, we show that GraphSPNs consistently outperform the traditional approach based on undirected graphical models, successfully disambiguating information in global semantic maps built from uncertain, noisy local evidence. We further exploit the probabilistic nature of the model to infer marginal distributions over semantic descriptions of as yet unexplored places and detect spatial environment conﬁgurations that are novel and incongruent with the known evidence.

NeurIPS Conference 2016 Conference Paper

A Probabilistic Model of Social Decision Making based on Reward Maximization

Koosha Khalvati
Seongmin Park
Jean-Claude Dreher
Rajesh Rao

A fundamental problem in cognitive neuroscience is how humans make decisions, act, and behave in relation to other humans. Here we adopt the hypothesis that when we are in an interactive social setting, our brains perform Bayesian inference of the intentions and cooperativeness of others using probabilistic representations. We employ the framework of partially observable Markov decision processes (POMDPs) to model human decision making in a social context, focusing specifically on the volunteer's dilemma in a version of the classic Public Goods Game. We show that the POMDP model explains both the behavior of subjects as well as neural activity recorded using fMRI during the game. The decisions of subjects can be modeled across all trials using two interpretable parameters. Furthermore, the expected reward predicted by the model for each subject was correlated with the activation of brain areas related to reward expectation in social interactions. Our results suggest a probabilistic basis for human social decision making within the framework of expected reward maximization.

NeurIPS Conference 2015 Conference Paper

A Bayesian Framework for Modeling Confidence in Perceptual Decision Making

Koosha Khalvati
Rajesh Rao

The degree of confidence in one's choice or decision is a critical aspect of perceptual decision making. Attempts to quantify a decision maker's confidence by measuring accuracy in a task have yielded limited success because confidence and accuracy are typically not equal. In this paper, we introduce a Bayesian framework to model confidence in perceptual decision making. We show that this model, based on partially observable Markov decision processes (POMDPs), is able to predict confidence of a decision maker based only on the data available to the experimenter. We test our model on two experiments on confidence-based decision making involving the well-known random dots motion discrimination task. In both experiments, we show that our model's predictions closely match experimental data. Additionally, our model is also consistent with other phenomena such as the hard-easy effect in perceptual decision making.

NeurIPS Conference 2014 Conference Paper

Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks

Yanping Huang
Rajesh Rao

We propose a two-layer spiking network capable of performing approximate inference and learning for a hidden Markov model. The lower layer sensory neurons detect noisy measurements of hidden world states. The higher layer neurons with recurrent connections infer a posterior distribution over world states from spike trains generated by sensory neurons. We show how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in the population of inference neurons represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution of hidden state. The model provides a functional explanation for the Poisson-like noise commonly observed in cortical responses. Uncertainties in spike times provide the necessary variability for sampling during inference. Unlike previous models, the hidden world state is not observed by the sensory neurons, and the temporal dynamics of the hidden state is unknown. We demonstrate how this network can sequentially learn the hidden Markov model using a spike-timing dependent Hebbian learning rule and achieve power-law convergence rates.

NeurIPS Conference 2012 Conference Paper

How Prior Probability Influences Decision Making: A Unifying Probabilistic Model

Yanping Huang
Timothy Hanks
Mike Shadlen
Abram Friesen
Rajesh Rao

How does the brain combine prior knowledge with sensory evidence when making decisions under uncertainty? Two competing descriptive models have been proposed based on experimental data. The first posits an additive offset to a decision variable, implying a static effect of the prior. However, this model is inconsistent with recent data from a motion discrimination task involving temporal integration of uncertain sensory evidence. To explain this data, a second model has been proposed which assumes a time-varying influence of the prior. Here we present a normative model of decision making that incorporates prior knowledge in a principled way. We show that the additive offset model and the time-varying prior model emerge naturally when decision making is viewed within the framework of partially observable Markov decision processes (POMDPs). Decision making in the model reduces to (1) computing beliefs given observations and prior information in a Bayesian manner, and (2) selecting actions based on these beliefs to maximize the expected sum of future rewards. We show that the model can explain both data previously explained using the additive offset model as well as more recent data on the time-varying influence of prior knowledge on decision making.

NeurIPS Conference 2010 Conference Paper

A rational decision making framework for inhibitory control

Pradeep Shenoy
Angela Yu
Rajesh Rao

Intelligent agents are often faced with the need to choose actions with uncertain consequences, and to modify those actions according to ongoing sensory processing and changing task demands. The requisite ability to dynamically modify or cancel planned actions is known as inhibitory control in psychology. We formalize inhibitory control as a rational decision-making problem, and apply to it to the classical stop-signal task. Using Bayesian inference and stochastic control tools, we show that the optimal policy systematically depends on various parameters of the problem, such as the relative costs of different action choices, the noise level of sensory inputs, and the dynamics of changing environmental demands. Our normative model accounts for a range of behavioral data in humans and animals in the stop-signal task, suggesting that the brain implements statistically optimal, dynamically adaptive, and reward-sensitive decision-making in the context of inhibitory control problems.

NeurIPS Conference 2006 Conference Paper

Learning Nonparametric Models for Probabilistic Imitation

David Grimes
Daniel Rashid
Rajesh Rao

Learning by imitation represents an important mechanism for rapid acquisition of new behaviors in humans and robots. A critical requirement for learning by imitation is the ability to handle uncertainty arising from the observation process as well as the imitator's own dynamics and interactions with the environment. In this paper, we present a new probabilistic method for inferring imitative actions that takes into account both the observations of the teacher as well as the imitator's dynamics. Our key contribution is a nonparametric learning method which generalizes to systems with very different dynamics. Rather than relying on a known forward model of the dynamics, our approach learns a nonparametric forward model via exploration. Leveraging advances in approximate inference in graphical models, we show how the learned forward model can be directly used to plan an imitating sequence. We provide experimental results for two systems: a biomechanical model of the human arm and a 25-degrees-of-freedom humanoid robot. We demonstrate that the proposed method can be used to learn appropriate motor inputs to the model arm which imitates the desired movements. A second set of results demonstrates dynamically stable full-body imitation of a human teacher by the humanoid robot.

NeurIPS Conference 2005 Conference Paper

Goal-Based Imitation as Probabilistic Inference over Graphical Models

Deepak Verma
Rajesh Rao

Humans are extremely adept at learning new skills by imitating the actions of others. A progression of imitative abilities has been observed in children, ranging from imitation of simple body movements to goalbased imitation based on inferring intent. In this paper, we show that the problem of goal-based imitation can be formulated as one of inferring goals and selecting actions using a learned probabilistic graphical model of the environment. We first describe algorithms for planning actions to achieve a goal state using probabilistic inference. We then describe how planning can be used to bootstrap the learning of goal-dependent policies by utilizing feedback from the environment. The resulting graphical model is then shown to be powerful enough to allow goal-based imitation. Using a simple maze navigation task, we illustrate how an agent can infer the goals of an observed teacher and imitate the teacher even when the goals are uncertain and the demonstration is incomplete.

NeurIPS Conference 2005 Conference Paper

Learning Shared Latent Structure for Image Synthesis and Robotic Imitation

Aaron Shon
Keith Grochow
Aaron Hertzmann
Rajesh Rao

We propose an algorithm that uses Gaussian process regression to learn common hidden structure shared between corresponding sets of heterogenous observations. The observation spaces are linked via a single, reduced-dimensionality latent variable space. We present results from two datasets demonstrating the algorithms's ability to synthesize novel data from learned correspondences. We first show that the method can learn the nonlinear mapping between corresponding views of objects, filling in missing data as needed to synthesize novel views. We then show that the method can learn a mapping between human degrees of freedom and robotic degrees of freedom for a humanoid robot, allowing robotic imitation of human poses from motion capture data.

NeurIPS Conference 2004 Conference Paper

Dynamic Bayesian Networks for Brain-Computer Interfaces

Pradeep Shenoy
Rajesh Rao

We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN) can be used to infer probability distributions over brain- and body-states during planning and execution of actions. The DBN is learned directly from observed data and allows measured signals such as EEG and EMG to be interpreted in terms of internal states such as intent to move, preparatory activity, and movement execution. Unlike traditional classification-based approaches to BCI, the proposed approach (1) allows continuous tracking and predic- tion of internal states over time, and (2) generates control signals based on an entire probability distribution over states rather than binary yes/no decisions. We present preliminary results of brain- and body-state es- timation using simultaneous EEG and EMG signals recorded during a self-paced left/right hand movement task.

NeurIPS Conference 2004 Conference Paper

Hierarchical Bayesian Inference in Networks of Spiking Neurons

Rajesh Rao

There is growing evidence from psychophysical and neurophysiological studies that the brain utilizes Bayesian principles for inference and de- cision making. An important open question is how Bayesian inference for arbitrary graphical models can be implemented in networks of spik- ing neurons. In this paper, we show that recurrent networks of noisy integrate-and-fire neurons can perform approximate Bayesian inference for dynamic and hierarchical graphical models. The membrane potential dynamics of neurons is used to implement belief propagation in the log domain. The spiking probability of a neuron is shown to approximate the posterior probability of the preferred state encoded by the neuron, given past inputs. We illustrate the model using two examples: (1) a motion de- tection network in which the spiking probability of a direction-selective neuron becomes proportional to the posterior probability of motion in a preferred direction, and (2) a two-level hierarchical network that pro- duces attentional effects similar to those observed in visual cortical areas V2 and V4. The hierarchical model offers a new Bayesian interpretation of attentional modulation in V2 and V4.

NeurIPS Conference 2002 Conference Paper

A Bilinear Model for Sparse Coding

David Grimes
Rajesh Rao

Recent algorithms for sparse coding and independent component analy- sis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transfor- mations into account. As a result, they produce image codes that are redundant because the same feature is learned at multiple locations. We describe an algorithm for sparse coding based on a bilinear generative model of images. By explicitly modeling the interaction between im- age features and their transformations, the bilinear approach helps reduce redundancy in the image code and provides a basis for transformation- invariant vision. We present results demonstrating bilinear sparse coding of natural images. We also explore an extension of the model that can capture spatial relationships between the independent features of an ob- ject, thereby providing a new framework for parts-based object recogni- tion.

NeurIPS Conference 1999 Conference Paper

Predictive Sequence Learning in Recurrent Neocortical Circuits

Rajesh Rao
Terrence Sejnowski

Neocortical circuits are dominated by massive excitatory feedback: more than eighty percent of the synapses made by excitatory cortical neurons are onto other excitatory cortical neurons. Why is there such massive re(cid: 173) current excitation in the neocortex and what is its role in cortical compu(cid: 173) tation? Recent neurophysiological experiments have shown that the plas(cid: 173) ticity of recurrent neocortical synapses is governed by a temporally asym(cid: 173) metric Hebbian learning rule. We describe how such a rule may allow the cortex to modify recurrent synapses for prediction of input sequences. The goal is to predict the next cortical input from the recent past based on previous experience of similar input sequences. We show that a temporal difference learning rule for prediction used in conjunction with dendritic back-propagating action potentials reproduces the temporally asymmet(cid: 173) ric Hebbian plasticity observed physiologically. Biophysical simulations demonstrate that a network of cortical neurons can learn to predict mov(cid: 173) ing stimuli and develop direction selective responses as a consequence of learning. The space-time response properties of model neurons are shown to be similar to those of direction selective cells in alert monkey VI.

NeurIPS Conference 1998 Conference Paper

Learning Lie Groups for Invariant Visual Perception

Rajesh Rao
Daniel Ruderman

One of the most important problems in visual perception is that of visual in(cid: 173) variance: how are objects perceived to be the same despite undergoing transfor(cid: 173) mations such as translations, rotations or scaling? In this paper, we describe a Bayesian method for learning invariances based on Lie group theory. We show that previous approaches based on first-order Taylor series expansions of inputs can be regarded as special cases of the Lie group approach, the latter being ca(cid: 173) pable of handling in principle arbitrarily large transfonnations. Using a matrix(cid: 173) exponential based generative model of images, we derive an unsupervised al(cid: 173) gorithm for learning Lie group operators from input data containing infinites(cid: 173) imal transfonnations. The on-line unsupervised learning algorithm maximizes the posterior probability of generating the training data. We provide experimen(cid: 173) tal results suggesting that the proposed method can learn Lie group operators for handling reasonably large I-D translations and 2-D rotations.

NeurIPS Conference 1997 Conference Paper

Correlates of Attention in a Model of Dynamic Visual Recognition

Rajesh Rao

Given a set of objects in the visual field, how does the the visual system learn to attend to a particular object of interest while ignoring the rest? How are occlusions and background clutter so effortlessly discounted for when rec(cid: 173) ognizing a familiar object? In this paper, we attempt to answer these ques(cid: 173) tions in the context of a Kalman filter-based model of visual recognition that has previously proved useful in explaining certain neurophysiological phe(cid: 173) nomena such as endstopping and related extra-classical receptive field ef(cid: 173) fects in the visual cortex. By using results from the field of robust statistics, we describe an extension of the Kalman filter model that can handle multiple objects in the visual field. The resulting robust Kalman filter model demon(cid: 173) strates how certain forms of attention can be viewed as an emergent prop(cid: 173) erty of the interaction between top-down expectations and bottom-up sig(cid: 173) nals. The model also suggests functional interpretations of certain attention(cid: 173) related effects that have been observed in visual cortical neurons. Exper(cid: 173) imental results are provided to help demonstrate the ability of the model to perform robust segmentation and recognition of objects and image se(cid: 173) quences in the presence of varying degrees of occlusions and clutter.

NeurIPS Conference 1995 Conference Paper

Modeling Saccadic Targeting in Visual Search

Rajesh Rao
Gregory Zelinsky
Mary Hayhoe
Dana Ballard

Visual cognition depends criticalIy on the ability to make rapid eye movements known as saccades that orient the fovea over targets of interest in a visual scene. Saccades are known to be ballistic: the pattern of muscle activation for foveating a prespecified target location is computed prior to the movement and visual feedback is precluded. Despite these distinctive properties, there has been no general model of the saccadic targeting strategy employed by the human visual system during visual search in natural scenes. This paper proposes a model for saccadic targeting that uses iconic scene representations derived from oriented spatial filters at multiple scales. Visual search proceeds in a coarse-to-fine fashion with the largest scale filter responses being compared first. The model was empirically tested by comparing its perfonnance with actual eye movement data from human subjects in a natural visual search task; preliminary results indicate substantial agreement between eye movements predicted by the model and those recorded from human subjects.

NeurIPS Conference 1994 Conference Paper

Learning Saccadic Eye Movements Using Multiscale Spatial Filters

Rajesh Rao
Dana Ballard

We describe a framework for learning saccadic eye movements using a photometric representation of target points in natural scenes. The rep(cid: 173) resentation takes the form of a high-dimensional vector comprised of the responses of spatial filters at different orientations and scales. We first demonstrate the use of this response vector in the task of locating pre(cid: 173) viously foveated points in a scene and subsequently use this property in a multisaccade strategy to derive an adaptive motor map for delivering accurate saccades.