Each month, we provide up to £2,000 in grant money to early career researchers in quantitative disciplines. Hear from our April grant winners.
Read articleICML 2024: Research Paper Reviews
Machine learning is a rapidly evolving field. To stay ahead of the curve, we actively encourage our quantitative researchers and machine learning engineers to attend conferences like ICML so they can engage in the latest cutting-edge research.
In this ICML paper review series, our team share their insights on the most interesting research and papers presented at the conference. They discuss the latest advancements in ML, offering a comprehensive overview of the field and where it is heading. Through this series, you will gain valuable insights into the latest trends and developments in ML.
Follow the links to read each set of ICML 2024 paper reviews.
Paper review #1
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- Arrows of Time for Large Language Models
- Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Yousuf, Machine Learning Engineer
Paper review #2
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- Compute Better Spent: Replacing Dense Layers with Structured Matrices
- Emergent Equivariance in Deep Ensembles
Danny, Machine Learning Engineer
Paper review #3
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- A Universal Class of Sharpness-Aware Minimization Algorithms
- Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks
Jonathan, Software Engineer
Paper review #4
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- Trained Random Forests Completely Reveal your Dataset
- Test-of-time Award: DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Evgeni, Senior Quantitative Researcher
Paper review #5
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- Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
- Physics of Language Models: Part 3.1, Knowledge Storage and Extraction
Michael, Scientific Director
Paper review #6
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- I/O Complexity of Attention, or How Optimal is Flash Attention?
- Simple Linear Attention Language Models Balance the Recall-Throughput Tradeoff
Fabian, Senior Quantitative Researcher
Paper review #7
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- Offline Actor-Critic Reinforcement Learning Scales to Large Models
- Information-Directed Pessimism for Offline Reinforcement Learning
Ingmar, Quantitative Researcher
Paper review #8
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- Better & Faster Large Language Models via Multi-token Prediction
- Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
Oliver, Quantitative Researcher
Latest News
Hear from our Head of Forecasting Engineering on why the term "tech debt" has outlived its usefulness. In this blog, he explores why we should move away from generic labels and instead ask more precise, value-driven questions that lead to meaningful improvements in engineering and business outcomes.
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Each month, we provide up to £2,000 in grant money to early career researchers in quantitative disciplines. Hear from our March grant winners.
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