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Found 16 papers, 6 papers with code
MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension
Large language models are playing an increasingly significant role in molecular research, yet existing models often generate erroneous information, posing challenges to accurate molecular comprehension.
SGE: Structured Light System Based on Gray Code with an Event Camera
Event camera, as a device that quickly responds to intensity changes, provides a new solution for structured light (SL) systems.
GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System
of a cascade RS, when a user triggers a request, we define two actions that determine the computation: (1) the trained instances of models with different computational complexity; and (2) the number of items to be inferred in the stage.
InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery
The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in reshaping interactions with complex molecular data.
Ranked #20 on
Molecule Captioning
on ChEBI-20
OptScaler: A Hybrid Proactive-Reactive Framework for Robust Autoscaling in the Cloud
The reactive module provides a self-tuning estimator of CPU utilization to the optimization model.
Leveraging Contextual Information for Effective Entity Salience Detection
In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document.
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation.
Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking
Entity Linking (EL) is a fundamental task for Information Extraction and Knowledge Graphs.
Locality Preserving Multiview Graph Hashing for Large Scale Remote Sensing Image Search
This article proposes a multiview hashing with learnable parameters to retrieve the queried images for a large-scale remote sensing dataset.
EDUKG: a Heterogeneous Sustainable K-12 Educational Knowledge Graph
To tackle these issues, we propose EDUKG, a heterogeneous sustainable K-12 Educational Knowledge Graph.
A framework for massive scale personalized promotion
In order to do effective optimization in the second stage, counterfactual prediction and noise-reduction are essential for the first stage.
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes.
Active Deep Learning on Entity Resolution by Risk Sampling
Building upon the recent advances in risk analysis for ER, which can provide a more refined estimate on label misprediction risk than the simpler classifier outputs, we propose a novel AL approach of risk sampling for ER.
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning
Despite the significant progress of deep reinforcement learning (RL) in solving sequential decision making problems, RL agents often overfit to training environments and struggle to adapt to new, unseen environments.
Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards
While recent progress in deep reinforcement learning has enabled robots to learn complex behaviors, tasks with long horizons and sparse rewards remain an ongoing challenge.
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
