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Found 24 papers, 14 papers with code
Retrieval-augmented Encoders for Extreme Multi-label Text Classification
In contrast, the dual-encoder (DE) model maps input and label text into a shared embedding space for better generalization (i. e., the capability of predicting tail labels with limited training data), but may fall short at memorization.
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models
For product search, PEFA improves the Recall@100 of the fine-tuned ERMs by an average of 5. 3% and 14. 5%, for PEFA-XS and PEFA-XL, respectively.
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering
Recent advances in few-shot question answering (QA) mostly rely on the power of pre-trained large language models (LLMs) and fine-tuning in specific settings.
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances.
Extreme Multi-Label Classification
MUlTI-LABEL-ClASSIFICATION
+1
Uncertainty in Extreme Multi-label Classification
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making.
Extreme Zero-Shot Learning for Extreme Text Classification
To learn the semantic embeddings of instances and labels with raw text, we propose to pre-train Transformer-based encoders with self-supervised contrastive losses.
Multi Label Text Classification
Multi-Label Text Classification
+2
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.
Ranked #2 on
Node Property Prediction
on ogbn-papers100M
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
Despite leveraging pre-trained transformer models for text representation, the fine-tuning procedure of transformer models on large label space still has lengthy computational time even with powerful GPUs.
Multi Label Text Classification
Multi-Label Text Classification
+2
Label Disentanglement in Partition-based Extreme Multilabel Classification
Partition-based methods are increasingly-used in extreme multi-label classification (XMC) problems due to their scalability to large output spaces (e. g., millions or more).
Extreme Multi-label Learning for Semantic Matching in Product Search
In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products.
Extreme Multi-Label Classification
MUlTI-LABEL-ClASSIFICATION
+1
PECOS: Prediction for Enormous and Correlated Output Spaces
In this paper, we propose the Prediction for Enormous and Correlated Output Spaces (PECOS) framework, a versatile and modular machine learning framework for solving prediction problems for very large output spaces, and apply it to the eXtreme Multilabel Ranking (XMR) problem: given an input instance, find and rank the most relevant items from an enormous but fixed and finite output space.
Kernel Stein Generative Modeling
NCK is crucial for successful inference with SVGD in high dimension, as it adapts the kernel to the noise level of the score estimate.
Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks
Keywords: Multivariate Time Series, Change-point Detection, Graph Neural Networks
Pre-training Tasks for Embedding-based Large-scale Retrieval
We consider the large-scale query-document retrieval problem: given a query (e. g., a question), return the set of relevant documents (e. g., paragraphs containing the answer) from a large document corpus.
XL-Editor: Post-editing Sentences with XLNet
While neural sequence generation models achieve initial success for many NLP applications, the canonical decoding procedure with left-to-right generation order (i. e., autoregressive) in one-pass can not reflect the true nature of human revising a sentence to obtain a refined result.
Taming Pretrained Transformers for Extreme Multi-label Text Classification
However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue.
Extreme Multi-Label Classification
General Classification
+4
Implicit Kernel Learning
While learning the kernel in a data driven way has been investigated, in this paper we explore learning the spectral distribution of kernel via implicit generative models parametrized by deep neural networks.
Kernel Change-point Detection with Auxiliary Deep Generative Models
Detecting the emergence of abrupt property changes in time series is a challenging problem.
SeCSeq: Semantic Coding for Sequence-to-Sequence based Extreme Multi-label Classification
To circumvent the softmax bottleneck, SeCSeq compresses labels into sequences of semantic-aware compact codes, on which Seq2Seq models are trained.
Extreme Multi-Label Classification
MUlTI-LABEL-ClASSIFICATION
Contextual Encoding for Translation Quality Estimation
In this paper, propose a method to effectively encode the local and global contextual information for each target word using a three-part neural network approach.
The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints
In this paper, we propose a low-rank coordinate descent approach to structured semidefinite programming with diagonal constraints.
MMD GAN: Towards Deeper Understanding of Moment Matching Network
In this paper, we propose to improve both the model expressiveness of GMMN and its computational efficiency by introducing adversarial kernel learning techniques, as the replacement of a fixed Gaussian kernel in the original GMMN.
Data-driven Random Fourier Features using Stein Effect
Large-scale kernel approximation is an important problem in machine learning research.
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.
Ranked #1 on
Univariate Time Series Forecasting
on Solar-Power
