Improvements & Evaluations on the MLCommons CloudMask Benchmark
- URL: http://arxiv.org/abs/2403.04553v1
- Date: Thu, 7 Mar 2024 14:48:48 GMT
- Title: Improvements & Evaluations on the MLCommons CloudMask Benchmark
- Abstract summary: We provide a description of the cloud-masking benchmark task, updated code, and the best model for this benchmark.
Results include the highest accuracy achieved on the NYU system as well as the average time taken for both training and inference.
- Score: 11.115005057843579
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we report the performance benchmarking results of deep
learning models on MLCommons' Science cloud-masking benchmark using a
high-performance computing cluster at New York University (NYU): NYU Greene.
MLCommons is a consortium that develops and maintains several scientific
benchmarks that can benefit from developments in AI. We provide a description
of the cloud-masking benchmark task, updated code, and the best model for this
benchmark when using our selected hyperparameter settings. Our benchmarking
results include the highest accuracy achieved on the NYU system as well as the
average time taken for both training and inference on the benchmark across
several runs/seeds. Our code can be found on GitHub. MLCommons team has been
kept informed about our progress and may use the developed code for their
future work.
Related papers
- Assessing and Improving the Representativeness of Code Generation Benchmarks Using Knowledge Units (KUs) of Programming Languages -- An Empirical Study [7.0773305889955616]
Large Language Models (LLMs) have shown impressive performance in code generation.<n>LLMs must understand and apply a wide range of language concepts.<n>If the concepts exercised in benchmarks are not representative of those used in real-world projects, evaluations may yield incomplete.
arXiv Detail & Related papers (2026-01-07T10:23:33Z) - InfoSynth: Information-Guided Benchmark Synthesis for LLMs [69.80981631587501]
Large language models (LLMs) have demonstrated significant advancements in reasoning and code generation.<n>Traditional benchmark creation relies on manual human effort, a process that is both expensive and time-consuming.<n>This work introduces Info Synth, a novel framework for automatically generating and evaluating reasoning benchmarks.
arXiv Detail & Related papers (2026-01-02T05:26:27Z) - Information Density Principle for MLLM Benchmarks [59.88484827926759]
We propose a critical principle of Information Density, which examines how much insight a benchmark can provide for the development of MLLMs.
Through a comprehensive analysis of more than 10,000 samples, we measured the information density of 19 MLLM benchmarks.
Experiments show that using the latest benchmarks in testing can provide more insight compared to previous ones, but there is still room for improvement in their information density.
arXiv Detail & Related papers (2025-03-13T05:58:41Z) - ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks [18.672482188560622]
MS-HAB is a holistic benchmark for low-level manipulation and in-home object rearrangement.
We support realistic low-level control and achieve over 3x the speed of prior magical grasp implementations at a fraction of the GPU memory usage.
arXiv Detail & Related papers (2024-12-09T01:29:24Z) - UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling [22.885385107905222]
We introduce UniBench, a unified implementation of 50+ vision-language model (VLM) benchmarks.
We showcase the utility of UniBench for measuring progress by evaluating nearly 60 publicly available vision-language models.
We also release an easy-to-run UniBench code-base with the full set of 50+ benchmarks and comparisons across 59 models as well as a distilled set of benchmarks that runs in 5 minutes on a single GPU.
arXiv Detail & Related papers (2024-08-09T01:41:05Z) - Generating Unseen Code Tests In Infinitum [1.0674604700001968]
We present a method for creating benchmark variations that generalize across coding tasks and programming languages.
We implement one benchmark, called textitauto-regression, for the task of text-to-code generation in Python.
arXiv Detail & Related papers (2024-07-29T08:11:20Z) - LiveBench: A Challenging, Contamination-Free LLM Benchmark [101.21578097087699]
We release LiveBench, the first benchmark that contains frequently-updated questions from recent information sources.
We evaluate many prominent closed-source models, as well as dozens of open-source models ranging from 0.5B to 110B in size.
Questions will be added and updated on a monthly basis, and we will release new tasks and harder versions of tasks over time.
arXiv Detail & Related papers (2024-06-27T16:47:42Z) - Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph [83.90988015005934]
Uncertainty quantification (UQ) is a critical component of machine learning (ML) applications.
We introduce a novel benchmark that implements a collection of state-of-the-art UQ baselines.
We conduct a large-scale empirical investigation of UQ and normalization techniques across nine tasks, and identify the most promising approaches.
arXiv Detail & Related papers (2024-06-21T20:06:31Z) - MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures [57.886592207948844]
We propose MixEval, a new paradigm for establishing efficient, gold-standard evaluation by strategically mixing off-the-shelf benchmarks.
It bridges (1) comprehensive and well-distributed real-world user queries and (2) efficient and fairly-graded ground-truth-based benchmarks, by matching queries mined from the web with similar queries from existing benchmarks.
arXiv Detail & Related papers (2024-06-03T05:47:05Z) - Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM [13.324171480106715]
EvoEval is a program synthesis benchmark suite created by evolving existing benchmarks into different targeted domains.
Our study shows that compared to the high performance obtained on standard benchmarks like HumanEval, there is a significant drop in performance.
We showcase various insights, including the brittleness of instruction-following models when encountering rewording or subtle changes.
arXiv Detail & Related papers (2024-03-28T03:10:39Z) - MLCommons Cloud Masking Benchmark with Early Stopping [0.837165016574332]
We report on work performed for the MLCommons Science Working Group on the cloud masking benchmark.
The benchmarks are conducted on the High Performance Computing Clusters of New York University and University of Virginia.
We provide a description of the cloud masking benchmark, as well as a summary of our submission to MLCommons on the benchmark experiment we conducted.
arXiv Detail & Related papers (2023-12-11T19:06:06Z) - LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits.
Most LLMs struggle on SummEdits, with performance close to random chance.
The best-performing model, GPT-4, is still 8% below estimated human performance.
arXiv Detail & Related papers (2023-05-23T21:50:06Z) - Forces are not Enough: Benchmark and Critical Evaluation for Machine
Learning Force Fields with Molecular Simulations [5.138982355658199]
Molecular dynamics (MD) simulation techniques are widely used for various natural science applications.
We benchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics.
arXiv Detail & Related papers (2022-10-13T17:59:03Z) - PDEBENCH: An Extensive Benchmark for Scientific Machine Learning [20.036987098901644]
We introduce PDEBench, a benchmark suite of time-dependent simulation tasks based on Partial Differential Equations (PDEs)
PDEBench comprises both code and data to benchmark the performance of novel machine learning models against both classical numerical simulations and machine learning baselines.
arXiv Detail & Related papers (2022-10-13T17:03:36Z) - CLUES: Few-Shot Learning Evaluation in Natural Language Understanding [81.63968985419982]
We introduce CLUES, a benchmark for evaluating the few-shot learning capabilities of NLU models.
We demonstrate that while recent models reach human performance when they have access to large amounts of labeled data, there is a huge gap in performance in the few-shot setting for most tasks.
arXiv Detail & Related papers (2021-11-04T00:43:15Z) - The Benchmark Lottery [114.43978017484893]
"A benchmark lottery" describes the overall fragility of the machine learning benchmarking process.
We show that the relative performance of algorithms may be altered significantly simply by choosing different benchmark tasks.
arXiv Detail & Related papers (2021-07-14T21:08:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.