Search Results for author: Sandeep Chinchali

Found 20 papers, 4 papers with code

Neuro-Symbolic Video Search

no code implementations16 Mar 2024 Minkyu Choi, Harsh Goel, Mohammad Omama, Yunhao Yang, Sahil Shah, Sandeep Chinchali

The unprecedented surge in video data production in recent years necessitates efficient tools to extract meaningful frames from videos for downstream tasks.

Retrieval

Time Weaver: A Conditional Time Series Generation Model

no code implementations5 Mar 2024 Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep Chinchali

Current approaches to time series generation often ignore this paired metadata, and its heterogeneity poses several practical challenges in adapting existing conditional generation approaches from the image, audio, and video domains to the time series domain.

Specificity Time Series +1

Online Foundation Model Selection in Robotics

no code implementations13 Feb 2024 Po-han Li, Oyku Selin Toprak, Aditya Narayanan, Ufuk Topcu, Sandeep Chinchali

We thus formulate a user-centric online model selection problem and propose a novel solution that combines an open-source encoder to output context and an online learning algorithm that processes this context.

Model Selection

Specification-Driven Video Search via Foundation Models and Formal Verification

no code implementations18 Sep 2023 Yunhao Yang, Jean-Raphaël Gaglione, Sandeep Chinchali, Ufuk Topcu

The increasing abundance of video data enables users to search for events of interest, e. g., emergency incidents.

Autonomous Driving

Data Games: A Game-Theoretic Approach to Swarm Robotic Data Collection

no code implementations7 Mar 2023 Oguzhan Akcin, Po-han Li, Shubhankar Agarwal, Sandeep Chinchali

Instead, we propose a cooperative data sampling strategy where geo-distributed AVs collaborate to collect a diverse ML training dataset in the cloud.

Autonomous Driving

Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks

no code implementations23 Jan 2023 Manan Gupta, Sandeep Chinchali, Paul Varkey, Jeffrey G. Andrews

Cellular networks are becoming increasingly heterogeneous with higher base station (BS) densities and ever more frequency bands, making BS selection and band assignment key decisions in terms of rate and coverage.

Model Predictive Control Reinforcement Learning (RL)

A Control Theoretic Approach to Infrastructure-Centric Blockchain Tokenomics

no code implementations23 Oct 2022 Oguzhan Akcin, Robert P. Streit, Benjamin Oommen, Sriram Vishwanath, Sandeep Chinchali

As such, the associated token rewards should gracefully scale with the size of the decentralized system, but should be carefully balanced with consumer demand to manage inflation and be designed to ultimately reach an equilibrium.

Online Poisoning Attacks Against Data-Driven Predictive Control

no code implementations19 Sep 2022 Yue Yu, Ruihan Zhao, Sandeep Chinchali, Ufuk Topcu

Data-driven predictive control (DPC) is a feedback control method for systems with unknown dynamics.

Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics

no code implementations20 Apr 2022 Christos Verginis, Cevahir Koprulu, Sandeep Chinchali, Ufuk Topcu

We develop a reinforcement-learning algorithm that infers a reward machine that encodes the underlying task while learning how to execute it, despite the uncertainties of the propositions' truth values.

Q-Learning reinforcement-learning +1

Task-Aware Network Coding Over Butterfly Network

no code implementations28 Jan 2022 Jiangnan Cheng, Sandeep Chinchali, Ao Tang

Classical network coding is largely task-agnostic -- the coding schemes mainly aim to faithfully reconstruct data at the receivers, regardless of what ultimate task the received data is used for.

ML-EXray: Visibility into ML Deployment on the Edge

no code implementations8 Nov 2021 Hang Qiu, Ioanna Vavelidou, Jian Li, Evgenya Pergament, Pete Warden, Sandeep Chinchali, Zain Asgar, Sachin Katti

The key challenge is that there is not much visibility into ML inference execution on edge devices, and very little awareness of potential issues during the edge deployment process.

Quantization

Task-aware Privacy Preservation for Multi-dimensional Data

1 code implementation5 Oct 2021 Jiangnan Cheng, Ao Tang, Sandeep Chinchali

Local differential privacy (LDP) can be adopted to anonymize richer user data attributes that will be input to sophisticated machine learning (ML) tasks.

Data Sharing and Compression for Cooperative Networked Control

1 code implementation NeurIPS 2021 Jiangnan Cheng, Marco Pavone, Sachin Katti, Sandeep Chinchali, Ao Tang

Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power generation.

Scheduling

Sampling Training Data for Continual Learning Between Robots and the Cloud

no code implementations12 Dec 2020 Sandeep Chinchali, Evgenya Pergament, Manabu Nakanoya, Eyal Cidon, Edward Zhang, Dinesh Bharadia, Marco Pavone, Sachin Katti

Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road construction sites, to steadily improve robotic perception models.

Cloud Computing Continual Learning +2

Task-relevant Representation Learning for Networked Robotic Perception

no code implementations6 Nov 2020 Manabu Nakanoya, Sandeep Chinchali, Alexandros Anemogiannis, Akul Datta, Sachin Katti, Marco Pavone

However, today's representations for sensory data are mostly designed for human, not robotic, perception and thus often waste precious compute or wireless network resources to transmit unimportant parts of a scene that are unnecessary for a high-level robotic task.

Motion Planning Representation Learning

Network Offloading Policies for Cloud Robotics: a Learning-based Approach

no code implementations15 Feb 2019 Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone

In this paper, we formulate a novel Robot Offloading Problem --- how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication?

Decision Making object-detection +1

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