10 Matching Annotations
- Jun 2024
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the inference efficiency improved by nearly three orders of magnitude or 1,000x in less than 2 years
for - stats - AI evolution - Math benchmark - 2022 to 2024
stats - AI evolution - Math benchmark - 2022 to 2024 - 50% increase in accuracy over 2 years - inference accuracy improved 1000x or 3 Orders Of Magnitude (OOM)
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there is essentially this Benchmark 00:09:58 called the math benchmark a set of difficult mathematic problems from a high school math competitions and when the Benchmark was released in 2021 gpt3 only got 5%
for - stats - AI - evolution - Math benchmark
stats - AI - evolution - Math benchmark - 2021 - GPT3 scored 5% - 2022 - scored 50% - 2024 - Gemini 1.5 Pro scored 90%
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- Oct 2023
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docdrop.org docdrop.org
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Beyond just audio recordings so for that reason two of our senior 00:15:02 researchers Benjamin Hoffman and Maddie cusumano have also developed a biologer benchmark data set and so a biologer is an animal born tag like the one in the image on the right here 00:15:14 and these produce very valuable data because they can inform us about animal ecophysiology and allow us to improve conservation by monitoring animal movements and behaviors with very high 00:15:27 resolution
- for: BEBE, biologger Ethogram Benchmark
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beans and 00:13:54 this is a benchmark of animal sounds and it's a collection of audio recordings from more than 250 species and this large aggregate data set is a way to 00:14:07 test tools for classification and detection and these are outstanding problems in bioacoustics that we desperately need solutions to
- for: BEANS, Benchmark of Animal Sounds
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- Sep 2023
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this is AVS the very first Foundation model for animal communication
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www.researchgate.net www.researchgate.net
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for: animal communication, AI - animal communication, bioacoustic
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title: BEAN: The Benchmark of Animal Sounds
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author
- Masato Hagiwara
- Benjamin Hoffman
- Jen-Yu Liu
- Maddie Cusimano
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Abstract
- The use of machine learning (ML) based techniques has become increasingly popular in the field of bioacoustics over the last years.
- Fundamental requirements for the successful application of ML based techniques are curated, agreed upon, high-quality datasets and benchmark tasks to be learned on a given dataset.
- However, the field of bioacoustics so far lacks such public benchmarks which cover multiple tasks and species to measure the performance of ML techniques in a controlled and standardized way and that allows for benchmarking newly proposed techniques to existing ones.
- Here, we propose BEANS (the BEnchmark of ANimal Sounds), a collection of bioacoustics tasks and public datasets, specifically designed to measure the performance of machine learning algorithms in the field of bioacoustics.
- The benchmark proposed here consists of two common tasks in bioacoustics:
- classification and
- detection.
- It includes 12 datasets covering various species, including
- birds,
- land and marine mammals,
- anurans, and insects.
- In addition to the datasets, we also present the performance of a set of standard ML methods as the baseline for task performance.
- The benchmark and baseline code is made publicly available at
- in the hope of establishing a new standard dataset for ML-based bioacoustic research.
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- Dec 2022
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www.zhihu.com www.zhihu.com
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java循环长度的相同、循环体代码相同的两次for循环的执行时间相差了100倍?
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- Feb 2020
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github.com github.com
- Jan 2020
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pubmed.ncbi.nlm.nih.gov pubmed.ncbi.nlm.nih.gov
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targeting one of three TSH ranges (0.34 to 2.50, 2.51 to 5.60, or 5.61 to 12.0 mU/L)
Note that they did not have a mild hyperthyroidism group, whereas they did have a mild hypothyroidism group.
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- Apr 2017
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rust-leipzig.github.io rust-leipzig.github.io