Local Look-Ahead Guidance via Verifier-in-the-Loop for Automated Theorem Proving

no code implementations • 12 Mar 2025 • Sara Rajaee, Kumar Pratik, Gabriele Cesa, Arash Behboodi

The most promising recent methods for AI reasoning require applying variants of reinforcement learning (RL) either on rolled out trajectories from the model, even for the step-wise rewards, or large quantities of human annotated trajectory data.

Automated Theorem Proving Reinforcement Learning (RL) +1

Equivariant Priors for Compressed Sensing with Unknown Orientation

no code implementations • 28 Jun 2022 • Anna Kuzina, Kumar Pratik, Fabio Valerio Massoli, Arash Behboodi

In compressed sensing, the goal is to reconstruct the signal from an underdetermined system of linear measurements.

compressed sensing Decoder

Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking

no code implementations • 26 Sep 2021 • Kumar Pratik, Rana Ali Amjad, Arash Behboodi, Joseph B. Soriaga, Max Welling

Through extensive experiments on CDL-B channel model, we show that the HKF can be used for tracking the channel over a wide range of Doppler values, matching Kalman filter performance with genie Doppler information.

RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection

1 code implementation • 30 Jun 2020 • Kumar Pratik, Bhaskar D. Rao, Max Welling

Each iterative unit is a neural computation module comprising of 3 sub-modules: the likelihood module, the encoder module, and the predictor module.