4 citations · 6 across the 7 of their papers we have counts for
7 papers
Modeling What Changes: Sparse, Residual World Models for Object-Centric Manipulation
Param Thakkar, Parsika Paresh Shah, Manisha Sushant Gote
Monolithic world models predict the entire next state at every step, spending capacity re-predicting the static majority of a scene and injecting error into it. We ask whether expl…
One Lens, Many Worlds : A Capability-Typed Interface for World-Model Interpretability
Bhavith Chandra Challagundla, Sanskar Pandey, Param Thakkar +7
World models are now built on substantially different computational substrates. Latent recurrent state-space models such as PlaNet and the Dreamer family compress observations into…
BhashaSetu: A Data-Centric Approach to Low-Resource Machine Translation
Param Thakkar, Anushka Yadav, Michael Tiemann +3
We present BhashaSetu, a linguistically enriched English--Marathi parallel dataset addressing persistent data limitations in low-resource neural machine translation (NMT). Marathi,…
HELIX: Scaling Raw Audio Understanding with Hybrid Mamba-Attention Beyond the Quadratic Limit
Khushiyant, Param Thakkar
Audio representation learning typically evaluates design choices such as input frontend, sequence backbone, and sequence length in isolation. We show that these axes are coupled, a…
BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
Azhar Ikhtiarudin, Aditi Das, Param Thakkar +1
We present BenchRL-QAS, a unified benchmarking framework for reinforcement learning (RL) in quantum architecture search (QAS) across a spectrum of variational quantum algorithm tas…
Personalized Recommendation Systems using Multimodal, Autonomous, Multi Agent Systems
Param Thakkar, Anushka Yadav
This paper describes a highly developed personalised recommendation system using multimodal, autonomous, multi-agent systems. The system focuses on the incorporation of futuristic…
Flood Prediction Using Classical and Quantum Machine Learning Models
Marek Grzesiak, Param Thakkar
This study investigates the potential of quantum machine learning to improve flood forecasting we focus on daily flood events along Germany's Wupper River in 2023 our approach comb…