Brokoslaw Laschowski

This paper introduces a framework for interpreting reward functions from inverse reinforcement learning, demonstrating its application to multi-agent social interactions.

This study evaluates diffusion latent representations for neural decoding, showing varying performance based on diffusion timesteps.

This paper introduces a brain-inspired framework for memory prioritization in neural networks based on valence, showing improved long-term memory and mitigation of the 'lost-in-the-middle' problem in language modeling.

This paper develops an EMG foundation model for neural decoding that outperforms subject-optimized models in both in-distribution and out-of-distribution evaluations.

This paper introduces a mixture model framework for domain-adaptive brain decoding, achieving state-of-the-art performance with less training data by optimizing mixture weights via convex optimization.

This paper develops a real-time neural controller using an EMG-driven musculoskeletal model to enable volitional control of robots and computers with high accuracy and low latency.

This paper develops a multimodal vision-language system for egocentric perception of walking environments, generating detailed image captions with user personalization.

The paper compares neural decoding algorithms for brain-machine interfaces to find the optimal combination of signal processing, feature extraction, and classifiers.