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Found 24 papers, 10 papers with code
Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge
Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively.
Learning Directed Acyclic Graphs from Partial Orderings
In general, learning the DAG structure is both computationally and statistically challenging.
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
In this paper, we propose FALCON, a novel combinatorial-optimization-based framework for network pruning that jointly takes into account model accuracy (fidelity), FLOPs, and sparsity constraints.
3D Object Visibility Prediction in Autonomous Driving
With the rapid advancement of hardware and software technologies, research in autonomous driving has seen significant growth.
Provable Filter for Real-world Graph Clustering
Motivated by this finding, we construct two graphs that are highly homophilic and heterophilic, respectively.
Robust Graph Structure Learning under Heterophily
In this regard, we propose a novel robust graph structure learning method to achieve a high-quality graph from heterophilic data for downstream tasks.
MLPs Compass: What is learned when MLPs are combined with PLMs?
While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain derived from the additional components of PLMs remains an open question in this field.
Rethinking Relation Classification with Graph Meaning Representations
In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research.
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features
The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD).
Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction
Document-level event argument extraction is a crucial yet challenging task within the field of information extraction.
Multi-feature concatenation and multi-classifier stacking: an interpretable and generalizable machine learning method for MDD discrimination with rsfMRI
In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites.
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
To solve the MIP, we propose a custom nonlinear branch-and-bound (BnB) framework that solves node relaxations with tailored first-order methods.
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search
To deal with this challenge, we propose a novel, permutation-based local search method that can complement first-order methods in training any sparse gate, e. g., Hash routing, Top-k, DSelect-k, and COMET.
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization
Our approach, CHITA, extends the classical Optimal Brain Surgeon framework and results in significant improvements in speed, memory, and performance over existing optimization-based approaches for network pruning.
Eliminating Gradient Conflict in Reference-based Line-Art Colorization
To understand the instability in training, we detect the gradient flow of attention and observe gradient conflict among attention branches.
Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants
The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients.
DPGNN: Dual-Perception Graph Neural Network for Representation Learning
To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction.
Definite Non-Ancestral Relations and Structure Learning
Ancestral relations between variables play an important role in causal modeling.
Subgradient Regularized Multivariate Convex Regression at Scale
We present new large-scale algorithms for fitting a subgradient regularized multivariate convex regression function to $n$ samples in $d$ dimensions -- a key problem in shape constrained nonparametric regression with applications in statistics, engineering and the applied sciences.
Multi-graph Fusion for Multi-view Spectral Clustering
Most existing methods don't pay attention to the quality of the graphs and perform graph learning and spectral clustering separately.
Multiple Partitions Aligned Clustering
Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie.
Latent Multi-view Semi-Supervised Classification
To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method.
Low-rank Kernel Learning for Graph-based Clustering
By formulating graph construction and kernel learning in a unified framework, the graph and consensus kernel can be iteratively enhanced by each other.
On Causal Discovery with Equal Variance Assumption
Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variances.
Methodology Computation
