arXiv subjects

Sanjeev V. Namjoshi

Publications and source records attributed to Sanjeev V. Namjoshi.

4 recordsLinked to original sources

arXiv2026

Empathy Modeling in Active Inference Agents for Perspective-Taking and Alignment

Artificial agents that model other agents must predict their behavior and determine whether their outcomes matter within action selection. We introduce an active inference framework that separates these components by combining a history-conditioned Theory of Mind model with an explicit other-regarding valuation parameter, $λ$. We instantiate the framework in the Iterated Prisoner's Dilemma. The joint empathy configuration $(λ_i,λ_j)$ reorganizes the long-run cooperation landscape: sufficiently strong and symmetric other-regarding valuation supports sustained mutual cooperation, whereas strong asymmetry exposes the more empathic agent to systematic exploitation. Along the symmetric diagonal, cooperation exhibits a sharp but continuous finite-precision crossover. Fixed-partner sweeps reveal that the apparent cooperation boundary is path-dependent and that temporal variability is elevated where those paths cross it. Online Bayesian inference over opponent parameters modestly facilitates cooperation near the behavioral boundary but does not substitute for other-regarding valuation. Direct model comparison likewise shows that opponent-sensitive prediction at $λ=0$ does not generate cooperation. Planning depth has a partner-dependent effect: it slightly reduces cooperation when modeled reciprocity is weak but strongly increases cooperation against a reciprocating partner such as tit-for-tat. These results distinguish prediction, planning, and prosocial valuation as separable but interacting components of social agency. They also reveal a central limitation of unconditional empathic concern: the same valuation that stabilizes mutual cooperation creates predictable vulnerability when concern is not reciprocated.

physics.soc-ph↗

arXiv2023

A computational model of behavioral adaptation to solve the credit assignment problem

The adaptive fitness of an organism in its ecological niche is highly reliant upon its ability to associate an environmental or internal stimulus with a behavior response through reinforcement. This simple but powerful observation has been successfully applied in a number of contexts within computational neuroscience and reinforcement learning to model both human and animal behaviors. However, a critical challenge faced by these models is the credit assignment problem which asks how past behavior comes to be associated with a delayed reinforcement signal. In this paper we reformulate the credit assignment problem to ask how past stimuli come to be linked to adaptive behavioral responses in the context of a simple neuronal circuit. We propose a biologically plausible variant of a spiking neural network which can model a wide variety of behavioral, learning, and evolutionary phenomena. Our model suggests one fundamental mechanism, potentially in use in the brains of both simple and complex organisms, that would allow it to associate a behavior with an adaptive response. We present results that showcase the model's versatility and biological plausibility in a number of tasks related to classical and operant conditioning including behavioral chaining. We then provide further simulations to demonstrate how adaptive behaviors such as reflexes and simple category detection may have evolved using our model. Our results indicate the potential for further modifications and extensions of our model to replicate more sophisticated and biologically plausible behavioral, learning, and intelligence phenomena found throughout the animal kingdom.

q-bio.NC↗

arXiv2023

Serverless Federated Learning with flwr-serverless

Federated learning is becoming increasingly relevant and popular as we witness a surge in data collection and storage of personally identifiable information. Alongside these developments there have been many proposals from governments around the world to provide more protections for individuals' data and a heightened interest in data privacy measures. As deep learning continues to become more relevant in new and existing domains, it is vital to develop strategies like federated learning that can effectively train data from different sources, such as edge devices, without compromising security and privacy. Recently, the Flower (\texttt{Flwr}) Python package was introduced to provide a scalable, flexible, and easy-to-use framework for implementing federated learning. However, to date, Flower is only able to run synchronous federated learning which can be costly and time-consuming to run because the process is bottlenecked by client-side training jobs that are slow or fragile. Here, we introduce \texttt{flwr-serverless}, a wrapper around the Flower package that extends its functionality to allow for both synchronous and asynchronous federated learning with minimal modification to Flower's design paradigm. Furthermore, our approach to federated learning allows the process to run without a central server, which increases the domains of application and accessibility of its use. This paper presents the design details and usage of this approach through a series of experiments that were conducted using public datasets. Overall, we believe that our approach decreases the time and cost to run federated training and provides an easier way to implement and experiment with federated learning systems.

cs.LG↗

arXiv2022

A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI

Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MRI data. Our IRB-approved single-site retrospective study was comprised of patients (n=1,399) referred for MRI treatment-planning and gamma knife radiosurgery over 21 years. Contrast-enhanced T1-weighted and T2-weighted Fluid-Attenuated Inversion Recovery brain MRI exams (n=1,582) were preprocessed and input to the proposed deep learning workflow for tumor segmentation, modality transfer, and primary site classification into one of five classes. Ten-fold cross-validation generated overall AUC of 0.878 (95%CI:0.873,0.883), lung class AUC of 0.889 (95%CI:0.883,0.895), breast class AUC of 0.873 (95%CI:0.860,0.886), melanoma class AUC of 0.852 (95%CI:0.842,0.862), renal class AUC of 0.830 (95%CI:0.809,0.851), and other class AUC of 0.822 (95%CI:0.805,0.839). These data establish that whole-brain imaging features are discriminative to allow accurate diagnosis of the primary organ site of malignancy. Our end-to-end deep radiomic approach has great potential for classifying metastatic tumor types from whole-brain MRI images. Further refinement may offer an invaluable clinical tool to expedite primary cancer site identification for precision treatment and improved outcomes.

eess.IV↗