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Found 20 papers, 11 papers with code
Towards Socially Intelligent Agents with Mental State Transition and Human Value
One of which is to track the agent’s mental state transition and teach the agent to make decisions guided by its value like a human.
A Survey of Deep Learning for Mathematical Reasoning
Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life.
OpenD: A Benchmark for Language-Driven Door and Drawer Opening
We introduce OPEND, a benchmark for learning how to use a hand to open cabinet doors or drawers in a photo-realistic and physics-reliable simulation environment driven by language instruction.
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning
However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data.
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
We further design language models to learn to generate lectures and explanations as the chain of thought (CoT) to mimic the multi-hop reasoning process when answering ScienceQA questions.
Ranked #5 on
Science Question Answering
on ScienceQA
SAVAnet: Surgical Action-Driven Visual Attention Network for Autonomous Endoscope Control
Finally, the simulation results show that the SAVAnet can endow endoscope with surgeons’ visual attention to perform self-control in real time.
Triangular Character Animation Sampling with Motion, Emotion, and Relation
We propose a Spatial-Temporal And-Or graph (ST-AOG), a stochastic grammar model, to encode the contextual relationship between motion, emotion, and relation, forming a triangle in a conditional random field.
Structure Extraction in Task-Oriented Dialogues with Slot Clustering
Extracting structure information from dialogue data can help us better understand user and system behaviors.
Learning from the Tangram to Solve Mini Visual Tasks
Current pre-training methods in computer vision focus on natural images in the daily-life context.
ValueNet: A New Dataset for Human Value Driven Dialogue System
To the best of our knowledge, ValueNet is the first large-scale text dataset for human value modeling, and we are the first one trying to incorporate a value model into emotionally intelligent dialogue systems.
Exploring Forensic Dental Identification with Deep Learning
In this work, we pioneer to study deep learning for dental forensic identification based on panoramic radiographs.
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning
Also, we develop a strong IconQA baseline Patch-TRM that applies a pyramid cross-modal Transformer with input diagram embeddings pre-trained on the icon dataset.
Ranked #1 on
Visual Question Answering (VQA)
on IconQA
X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph
Different from single object reconstruction from photos, this task has the unique challenge of constructing multiple objects at high resolutions.
STAR: Sparse Transformer-based Action Recognition
The cognitive system for human action and behavior has evolved into a deep learning regime, and especially the advent of Graph Convolution Networks has transformed the field in recent years.
SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues
Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly.
Ranked #5 on
Dialog Relation Extraction
on DialogRE
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning
We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS).
Ranked #1 on
Mathematical Question Answering
on GeoS
Towards Socially Intelligent Agents with Mental State Transition and Human Utility
One of which is to track the agent's mental state transition and teach the agent to make decisions guided by its value like a human.
Atlas-aware ConvNetfor Accurate yet Robust Anatomical Segmentation
Second, we can largely boost the robustness of existing ConvNets, proved by: (i) testing on scans with synthetic pathologies, and (ii) training and evaluation on scans of different scanning setups across datasets.
Structured Attention for Unsupervised Dialogue Structure Induction
Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics.
Recurrent Neural Networks with Pre-trained Language Model Embedding for Slot Filling Task
In recent years, Recurrent Neural Networks (RNNs) based models have been applied to the Slot Filling problem of Spoken Language Understanding and achieved the state-of-the-art performances.
