Summary of Hedging Is Not All You Need: a Simple Baseline For Online Learning Under Haphazard Inputs, by Himanshu Buckchash et al.
Hedging Is Not All You Need: A Simple Baseline for Online Learning Under Haphazard Inputs
by Himanshu Buckchash, Momojit Biswas, Rohit Agarwal, Dilip K. Prasad
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel model called HapNet to handle haphazard streaming data from edge devices, which becomes inconsistent over time with missing, faulty, or new inputs reappearing. Recent methods rely on hedging-based solutions requiring specialized components like auxiliary dropouts and intricate network design. Instead, the authors approximate hedging with plain self-attention and develop a scalable model that doesn’t require online backpropagation or adapting to varying input types. The proposed approach is evaluated on five benchmarks, demonstrating competitive performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a tricky problem where data from edge devices gets messy over time. Right now, we use special techniques like hedging-based solutions and complicated network designs to deal with this issue. But the authors think they can simplify things by using something called self-attention instead of hedging. They came up with a new model called HapNet that works well for most cases, even when the data gets really unpredictable. |
Keywords
» Artificial intelligence » Backpropagation » Self attention
