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Researcher profile

Puneet K. Dokania

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Calibrating Deep Neural Networks using Focal Loss

    2020 · arXiv (Cornell University)

    Miscalibration - a mismatch between a model's confidence and its correctness - of Deep Neural Networks (DNNs) makes their predictions hard to rely on. Ideally, we want networks to be accurate, calibrated and confident. We …

  2. Continual Learning in Low-rank Orthogonal Subspaces

    2020 · arXiv (Cornell University)

    In continual learning (CL), a learner is faced with a sequence of tasks, arriving one after the other, and the goal is to remember all the tasks once the continual learning experience is finished. The …

  3. Computationally Budgeted Continual Learning: What Does Matter?

    2023 · arXiv (Cornell University)

    Continual Learning (CL) aims to sequentially train models on streams of incoming data that vary in distribution by preserving previous knowledge while adapting to new data. Current CL literature focuses on restricted access to previously …

  4. Online Continual Learning Without the Storage Constraint

    2023 · arXiv (Cornell University)

    Traditional online continual learning (OCL) research has primarily focused on mitigating catastrophic forgetting with fixed and limited storage allocation throughout an agent's lifetime. However, a broad range of real-world applications are primarily constrained by computational …

  5. Graph Inductive Biases in Transformers without Message Passing

    2023 · arXiv (Cornell University)

    Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph …

  6. Random Representations Outperform Online Continually Learned Representations

    2024 · arXiv (Cornell University)

    Continual learning has primarily focused on the issue of catastrophic forgetting and the associated stability-plasticity tradeoffs. However, little attention has been paid to the efficacy of continually learned representations, as representations are learned alongside classifiers …