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Discover the most cited and impactful AI research papers. Visualize interactive citation graphs, explore research connections, and navigate through paper networks across machine learning, computer vision, and natural language processing.
Kaiming He
X. Zhang
Shaoqing Ren
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Nikhila Ravi
Wan-Yen Lo
Ross B. Girshick
Laura Gustafson
Chloé Rolland
+7The Segment Anything Model (SAM) is introduced: a new task, model, and dataset for image segmentation, and its zero-shot performance is impressive – often competitive with or even superior to prior fully supervised results.
S. McKinney
Todor Markov
Jacob Menick
I. Sutskever
N. Keskar
+274GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs, is developed, a Transformer-based model pre-trained to predict the next token in a document which exhibits human-level performance on various professional and academic benchmarks.
Zewar Shah
Shan Zhiyong
Adnan
This project aims to provide an efficient and robust real-time emotion identification framework that makes use of paralinguistic factors such as intensity, pitch, and MFCC and employs Diffusion Map to reduce data redundancy and high dimensionality.
Noam Shazeer
Ashish Vaswani
Lukasz Kaiser
Jakob Uszkoreit
Niki Parmar
+3A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
Yuhai Wu
This chapter presents techniques for statistical machine learning using Support Vector Machines (SVM) to recognize the patterns and classify them, predicting structured objects using SVM, k-nearest neighbor method for classification, and Naive Bayes classifiers.
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Dataset for studying the relationship between music and lighting in live music performances
Introduction
Are current 3D object tracking methods truely robust enough for low-fidelity depth sensors like the iPhone LiDAR? We introduce DTTD-Mobile (fully compatible w/ YCB toolbox), a new benchmark built on real-world data captured from mobile devices; 18 objects observed in 100 videos with 47,668 sampled frames and 114,143 object annotations. We evaluate several popular methods—including BundleSDF, ES6D, MegaPose, and DenseFusion—and highlight their limitations in this challenging setting.
TimeGraph is a comprehensive suite of synthetic datasets designed to benchmark causal discovery algorithms on time-series data. The dataset captures real-world complexities by incorporating temporal dynamics such as trends, seasonality, and nonstationarity, as well as sampling challenges including irregular time intervals and structured missingness. It features diverse noise types, including Gaussian, heavy-tailed, and heteroskedastic variations, and supports scenarios with latent confounding to enable evaluation under partially observed systems. The underlying causal structures span both linear and nonlinear relationships, including polynomial and trigonometric forms.
LEMONADE is a large, expert-annotated dataset for event extraction from news articles in 20 languages: English, Spanish, Arabic, French, Italian, Russian, German, Turkish, Burmese, Indonesian, Ukrainian, Korean, Portuguese, Dutch, Somali, Nepali, Chinese, Persian, Hebrew, and Japanese.
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B-XAIC consists of 50K small molecules represented as graphs and includes 7 graph classification tasks, each with ground truth labels and corresponding explanations.
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Data Augmentation
EEG