Search Results
The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use
The recently released model, Claude 3. 5 Computer Use, stands out as the first frontier AI model to offer computer use in public beta as a graphical user interface (GUI) agent.
MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning
We evaluate a range of LLMs and prompting techniques on this dataset and characterize the gaps that remain for techniques like chain-of-thought to perform robust reasoning.
Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Ranked #2 on
Multimodal Machine Translation
on Multi30K
(BLUE (DE-EN) metric)
MojiTalk: Generating Emotional Responses at Scale
In this paper, we take a more radical approach: we exploit the idea of leveraging Twitter data that are naturally labeled with emojis.
End-to-end Learning for GMI Optimized Geometric Constellation Shape
Autoencoder-based geometric shaping is proposed that includes optimizing bit mappings.
MegaPortraits: One-shot Megapixel Neural Head Avatars
In this work, we advance the neural head avatar technology to the megapixel resolution while focusing on the particularly challenging task of cross-driving synthesis, i. e., when the appearance of the driving image is substantially different from the animated source image.
Chinese Spelling Correction as Rephrasing Language Model
However, we note a critical flaw in the process of tagging one character to another, that the correction is excessively conditioned on the error.
Does Chain-of-Thought Reasoning Help Mobile GUI Agent? An Empirical Study
Reasoning capabilities have significantly improved the performance of vision-language models (VLMs) in domains such as mathematical problem-solving, coding, and visual question-answering.
Do large language vision models understand 3D shapes?
Mainly it seems that the models can easily identify the same object with a different orientation as well as matching identical 3D shapes of the same orientation but with different materials and textures.
Early evidence of how LLMs outperform traditional systems on OCR/HTR tasks for historical records
We explore the ability of two LLMs -- GPT-4o and Claude Sonnet 3. 5 -- to transcribe historical handwritten documents in a tabular format and compare their performance to traditional OCR/HTR systems: EasyOCR, Keras, Pytesseract, and TrOCR.
