673 citations · 737 across the 10 of their papers we have counts for
10 papers
Scaling Laws of Motion Forecasting and Planning -- Technical Report
Mustafa Baniodeh, Kratarth Goel, Scott Ettinger +14
We study the empirical scaling laws of a family of encoder-decoder autoregressive transformer models on the task of joint motion forecasting and planning in the autonomous driving…
Direct Post-Training Preference Alignment for Multi-Agent Motion Generation Models Using Implicit Feedback from Pre-training Demonstrations
Ran Tian, Kratarth Goel
Recent advancements in LLMs have revolutionized motion generation models in embodied applications. While LLM-type auto-regressive motion generation models benefit from training sca…
Scaling Motion Forecasting Models with Ensemble Distillation
Scott Ettinger, Kratarth Goel, Avikalp Srivastava +1
Motion forecasting has become an increasingly critical component of autonomous robotic systems. Onboard compute budgets typically limit the accuracy of real-time systems. In this w…
MoST: Multi-modality Scene Tokenization for Motion Prediction
Norman Mu, Jingwei Ji, Zhenpei Yang +11
Many existing motion prediction approaches rely on symbolic perception outputs to generate agent trajectories, such as bounding boxes, road graph information and traffic lights. Th…
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou +3
Motion forecasting for autonomous driving is a challenging task because complex driving scenarios result in a heterogeneous mix of static and dynamic inputs. It is an open problem…
A Recurrent Latent Variable Model for Sequential Data
Junyoung Chung, Kyle Kastner, Laurent Dinh +3
In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoen…
A Novel Feature Selection and Extraction Technique for Classification
Kratarth Goel, Raunaq Vohra, Ainesh Bakshi
This paper presents a versatile technique for the purpose of feature selection and extraction - Class Dependent Features (CDFs). We use CDFs to improve the accuracy of classificati…
Home Automation Using SSVEP & Eye-Blink Detection Based Brain-Computer Interface
Kratarth Goel, Raunaq Vohra, Anant Kamath +1
In this paper, we present a novel brain computer interface based home automation system using two responses - Steady State Visually Evoked Potential (SSVEP) and the eye-blink artif…
Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN
Kratarth Goel, Raunaq Vohra, J. K. Sahoo
In this paper, we propose a generic technique to model temporal dependencies and sequences using a combination of a recurrent neural network and a Deep Belief Network. Our techniqu…
Learning Temporal Dependencies in Data Using a DBN-BLSTM
Kratarth Goel, Raunaq Vohra
Since the advent of deep learning, it has been used to solve various problems using many different architectures. The application of such deep architectures to auditory data is als…