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Yash Goel

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2 papers

2 author rows

TMLR Journal 2025 Journal Article

How to Upscale Neural Networks with Scaling Law?

Ayan Sengupta
Yash Goel
Tanmoy Chakraborty

Neural scaling laws have revolutionized the design and optimization of large-scale AI models by revealing predictable relationships between model size, dataset volume, and computational resources. Early research established power-law relationships in model performance, leading to compute-optimal scaling strategies. However, recent studies highlighted their limitations across architectures, modalities, and deployment contexts. Sparse models, mixture-of-experts, retrieval-augmented learning, and multimodal models often deviate from traditional scaling patterns. Moreover, scaling behaviors vary across domains such as vision, reinforcement learning, and fine-tuning, underscoring the need for more nuanced approaches. In this survey, we synthesize insights from current studies, examining the theoretical foundations, empirical findings, and practical implications of scaling laws. We also explore key challenges, including data efficiency, inference scaling, and architecture-specific constraints, advocating for adaptive scaling strategies tailored to real-world applications. We suggest that while scaling laws provide a useful guide, they do not always generalize across all architectures and training strategies.

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IROS Conference 2023 Conference Paper

Semantically Informed MPC for Context-Aware Robot Exploration

Yash Goel
Narunas Vaskevicius
Luigi Palmieri
Nived Chebrolu
Kai O. Arras
Cyrill Stachniss

We investigate the task of object goal navigation in unknown environments where a target object is given as a semantic label (e. g. find a couch). This task is challenging as it requires the robot to consider the semantic context in diverse settings (e. g. TVs are often nearby couches). Most of the prior work tackles this problem under the assumption of a discrete action policy whereas we present an approach with continuous control which brings it closer to real world applications. In this paper, we use information-theoretic model predictive control on dense cost maps to bring object goal navigation closer to real robots with kinodynamic constraints. We propose a deep neural network framework to learn cost maps that encode semantic context and guide the robot towards the target object. We also present a novel way of fusing mid-level visual representations in our architecture to provide additional semantic cues for cost map prediction. The experiments show that our method leads to more efficient and accurate goal navigation with higher quality paths than the reported baselines. The results also indicate the importance of mid-level representations for navigation by improving the success rate by 8 percentage points.

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