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Found 30 papers, 16 papers with code
You Only Sample Once: Taming One-Step Text-To-Image Synthesis by Self-Cooperative Diffusion GANs
We introduce YOSO, a novel generative model designed for rapid, scalable, and high-fidelity one-step image synthesis.
DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms
DTIAM, for the first time, provides a unified framework for accurate and robust prediction of drug-target interactions, binding affinities, and activation/inhibition mechanisms.
Machine Mindset: An MBTI Exploration of Large Language Models
We present a novel approach for integrating Myers-Briggs Type Indicator (MBTI) personality traits into large language models (LLMs), addressing the challenges of personality consistency in personalized AI.
Speak Like a Native: Prompting Large Language Models in a Native Style
Specifically, with AlignedCoT, we observe an average +3. 2\% improvement for \texttt{gpt-3. 5-turbo} compared to the carefully handcrafted CoT on multi-step reasoning benchmarks. Furthermore, we use AlignedCoT to rewrite the CoT text style in the training set, which improves the performance of Retrieval Augmented Generation by 3. 6\%. The source code and dataset is available at https://github. com/yangzhch6/AlignedCoT
Energy-Calibrated VAE with Test Time Free Lunch
To address these issues, we introduce a conditional EBM for calibrating the generative direction of VAE during training, without requiring it for the generation at test time.
Optimal Batched Best Arm Identification
Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i. e., $\delta>0$ is arbitrarily fixed), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero.
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning
Dual Queries first query LLM to obtain LLM-generated knowledge such as CoT, then query the retriever to obtain the final exemplars via both question and the knowledge.
Practical Parallel Algorithms for Non-Monotone Submodular Maximization
Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vision, and natural language processing.
How Fragile is Relation Extraction under Entity Replacements?
In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context.
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity
Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module.
Adaptive adversarial training method for improving multi-scale GAN based on generalization bound theory
Based on the drastic changes we found of the generalization error bound under different adversarial attacks and different training states, we proposed an adaptive training method which can greatly improve the image manipulation ability of multi-scale GANs.
PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation
The rational design of novel molecules with desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets.
Classification of datasets with imputed missing values: does imputation quality matter?
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial.
Diversifying Agent's Behaviors in Interactive Decision Models
In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model prior to their interactions.
Bayes in Wonderland! Predictive supervised classification inference hits unpredictability
The marginal Bayesian predictive classifiers (mBpc) as opposed to the simultaneous Bayesian predictive classifiers (sBpc), handle each data separately and hence tacitly assumes the independence of the observations.
Structure-Aware Label Smoothing for Graph Neural Networks
Representing a label distribution as a one-hot vector is a common practice in training node classification models.
R-BERT-CNN: Drug-target interactions extraction from biomedical literature
Drug-target interactions (DTIs) are critical for drug discovery and repurposing, which are often manually extracted from the experimental articles.
Ranked #1 on
DrugProt
on DrugProt
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).
Towards Making Deep Learning-based Vulnerability Detectors Robust
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.
Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment.
Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint
Second, we enhance the modified greedy algorithm to derive a data-dependent upper bound on the optimum.
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene
Based on it, we formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
Tensor Decomposition for Multi-agent Predictive State Representation
It is a concise knowledge representation that is well studied in a single-agent planning problem domain.
Efficient Approximation Algorithms for Adaptive Influence Maximization
In this paper, we propose the first practical algorithm for the adaptive IM problem that could provide the worst-case approximation guarantee of $1-\mathrm{e}^{\rho_b(\varepsilon-1)}$, where $\rho_b=1-(1-1/b)^b$ and $\varepsilon \in (0, 1)$ is a user-specified parameter.
Social and Information Networks
Keyphrase Extraction with Span-based Feature Representations
In addition, with the help of tokens, our model is able to extract overlapped keyphrases.
Highly fluorescent copper nanoclusters for sensing and bioimaging
Metal nanoclusters (NCs), typically consisting of a few to tens of metal atoms, bridge the gap between organometallic compounds and crystalline metal nanoparticles.
Numerical evaluation of the transition probability of the simple birth-and-death process
To overcome this difficulty we will rewrite the transition probability in terms of a Gaussian hypergeometric function and subsequently obtain a three-term recurrence relation for its accurate evaluation.
Numerical Analysis Numerical Analysis Probability Methodology 65Q30 (Primary), 60J80 (Secondary), 33C05, 62F10, 62M05
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit
In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification.
General Classification
Hierarchical Multi-label Classification
+4
Π-cyc: A Reference-free SNP Discovery Application using Parallel Graph Search
Specialised fast algorithms for efficient bubble search are needed for coloured de bruijn graph variant calling applications.
Computational Engineering, Finance, and Science Genomics
A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss
On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable.
Ranked #39 on
Abstractive Text Summarization
on CNN / Daily Mail
