Can Science Predict When a Study Won’t Hold Up?
Conducting research is hard; confirming the results is, too. And artificial intelligence isn’t yet ready to help, a major new study finds.
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Summarization model doesn't work
I try to run this below code (provided in Hugging Face’s LLMs course, lesson: Transformers, what can they do?) from transformers import pipeline summarize = pipeline("summarization") summarize( """ America has changed dramatically during recent years. Not only has the number of graduates in traditional engineering disciplines such as mechanical, civil, electrical, chemical, and aeronautical engineering declined, but in most of the premier American universities engineering curricula now concentrate on and encourage largely the study of engineering science. As a result, there are declining offerings in engineering subjects dealing with infrastructure, the environment, and related issues, and greater concentration on high technology subjects, largely supporting increasingly complex scientific
Scaling Agentic Memory to 5 Billion Vectors via Binary Quantization and Dynamic Wavelet Matrices
In a study, a new “dynamic wavelet matrix” was used as a vector database, where the memory grows only with log(σ) instead of with n. I considered building a KNN model with a huge memory, capable of holding, for example, 5 billion vectors. First, the words in the context window are converted into an embedding using deberta-v3-small. This is a fast encoder that also takes the position of the tokens into account (disentangled attention) and is responsible for the context in the model. The embedding is then converted into a bit sequence using binary quantization, where dimensions greater than 0 are converted to 1 and otherwise to 0. The advantage is that bit sequences are compressible and are entered into the dynamic wavelet matrix, where the memory grows only with log(σ). A response token is
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[D] ICML reviewer making up false claim in acknowledgement, what to do?
In a rebuttal acknowledgement we received, the reviewer made up a claim that our method performs worse than baselines with some hyperparameter settings. We did do a comprehensive list of hyperparameter comparisons and the reviewer's claim is not supported by what's presented in the paper. In this case what can we do? submitted by /u/dontknowwhattoplay [link] [comments]
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