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Found 18 papers, 9 papers with code
There’s a Time and Place for Reasoning Beyond the Image
Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture.
BLINK: Multimodal Large Language Models Can See but Not Perceive
We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations.
Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?
During the construction of the evidence, we purposefully replace semantic clues (entities) that may lead to the correct answer with distractor clues (evidence) that will not directly lead to the correct answer but require a chain-like reasoning process.
ImagenHub: Standardizing the evaluation of conditional image generation models
Recently, a myriad of conditional image generation and editing models have been developed to serve different downstream tasks, including text-to-image generation, text-guided image editing, subject-driven image generation, control-guided image generation, etc.
Typing on Any Surface: A Deep Learning-based Method for Real-Time Keystroke Detection in Augmented Reality
This paper proposes and validates a deep-learning based approach, that enables AR applications to accurately predict keystrokes from the user perspective RGB video stream that can be captured by any AR headset.
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge.
Dynamic Clue Bottlenecks: Towards Interpretable-by-Design Visual Question Answering
We propose the Dynamic Clue Bottleneck Model ( (DCLUB), a method that is designed towards an inherently interpretable VQA system.
There is a Time and Place for Reasoning Beyond the Image
For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understandings of the signs, the buildings, the crowds, and more.
An FEA surrogate model with Boundary Oriented Graph Embedding approach
In this work, we present a Boundary Oriented Graph Embedding (BOGE) approach for the Graph Neural Network (GNN) to serve as a general surrogate model for regressing physical fields and solving boundary value problems.
ASTRAL: Adversarial Trained LSTM-CNN for Named Entity Recognition
Under such circumstances, how to make full use of the information extracted by word embedding requires more in-depth research.
SRQA: Synthetic Reader for Factoid Question Answering
This model enhances the question answering system in the multi-document scenario from three aspects: model structure, optimization goal, and training method, corresponding to Multilayer Attention (MA), Cross Evidence (CE), and Adversarial Training (AT) respectively.
Design Challenges in Low-resource Cross-lingual Entity Linking
Cross-lingual Entity Linking (XEL), the problem of grounding mentions of entities in a foreign language text into an English knowledge base such as Wikipedia, has seen a lot of research in recent years, with a range of promising techniques.
Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings
In this paper, we tackle the problem of {``}root extraction{''} from words in the Semitic language family.
AlphaGomoku: An AlphaGo-based Gomoku Artificial Intelligence using Curriculum Learning
In this project, we combine AlphaGo algorithm with Curriculum Learning to crack the game of Gomoku.
A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension
Meanwhile, we construct A3Net for the WebQA dataset.
A Machine Learning Framework for Stock Selection
The effectiveness of the stock selection strategy is validated in Chinese stock market in both statistical and practical aspects, showing that: 1) Stacking outperforms other models reaching an AUC score of 0. 972; 2) Genetic Algorithm picks a subset of 114 features and the prediction performances of all models remain almost unchanged after the selection procedure, which suggests some features are indeed redundant; 3) LR and DNN are radical models; RF is risk-neutral model; Stacking is somewhere between DNN and RF.
Robust Log-Optimal Strategy with Reinforcement Learning
We proposed a new Portfolio Management method termed as Robust Log-Optimal Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by approximating the traditional objective function with quadratic Taylor expansion.
Language Distribution Prediction based on Batch Markov Monte Carlo Simulation with Migration
To model the randomness of language spreading, we propose the Batch Markov Monte Carlo Simulation with Migration(BMMCSM) algorithm, in which each agent is treated as a language stack.
