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Found 13 papers, 8 papers with code
Minimalist Softmax Attention Provably Learns Constrained Boolean Functions
We study the computational limits of learning $k$-bit Boolean functions (specifically, $\mathrm{AND}$, $\mathrm{OR}$, and their noisy variants), using a minimalist single-head softmax-attention mechanism, where $k=\Theta(d)$ relevant bits are selected from $d$ inputs.
On the Perception Bottleneck of VLMs for Chart Understanding
Chart understanding requires models to effectively analyze and reason about numerical data, textual elements, and complex visual components.
A LSTM-Transformer Model for pulsation control of pVADs
(2)The pulsation time characteristic points predicted by the LSTM-Transformer Model shows a maximum prediction error of 1. 78ms, which is significantly lower than other methods.
Diving into Self-Evolving Training for Multimodal Reasoning
Our analysis leads to a set of best practices for each factor, aimed at optimizing multimodal reasoning.
PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-training
The interest in developing small language models (SLM) for on-device deployment is fast growing.
Small Language Models: Survey, Measurements, and Insights
Small language models (SLMs), despite their widespread adoption in modern smart devices, have received significantly less academic attention compared to their large language model (LLM) counterparts, which are predominantly deployed in data centers and cloud environments.
DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving
Solving mathematical problems requires advanced reasoning abilities and presents notable challenges for large language models.
Ranked #4 on
Natural Questions
on TheoremQA
(using extra training data)
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
Leveraging our new pipeline, we create, to the best of our knowledge, the first one-step diffusion-based text-to-image generator with SD-level image quality, achieving an FID (Frechet Inception Distance) of $23. 3$ on MS COCO 2017-5k, surpassing the previous state-of-the-art technique, progressive distillation, by a significant margin ($37. 2$ $\rightarrow$ $23. 3$ in FID).
FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours
In this work, we present FastFold, an efficient implementation of AlphaFold for both training and inference.
Deep Learning for Frame Error Prediction using a DARPA Spectrum Collaboration Challenge (SC2) Dataset
We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation in the final scrimmages of the DARPA SC2 challenge.
Efficient Training of Deep Classifiers for Wireless Source Identification using Test SNR Estimates
We study efficient deep learning training algorithms that process received wireless signals, if a test Signal to Noise Ratio (SNR) estimate is available.
Approximate Query Service on Autonomous IoT Cameras
Elf is a runtime for an energy-constrained camera to continuously summarize video scenes as approximate object counts.
Databases
Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection
We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi.
