Search Results for author:
Found 36 papers, 16 papers with code
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.
Representer Point Selection for Explaining Regularized High-dimensional Models
We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model in terms of importance weights for each of the training samples.
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.
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.
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search.
Accelerating Primal-dual Methods for Regularized Markov Decision Processes
Entropy regularized Markov decision processes have been widely used in reinforcement learning.
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
DRONE: Data-aware Low-rank Compression for Large NLP Models
In addition to compressing standard models, out method can also be used on distilled BERT models to further improve compression rate.
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.
Enterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees
In industrial applications, these models operate at extreme scales, where every bit of performance is critical.
Data-aware Low-Rank Compression for Large NLP Models
In this paper, we observe that the learned representation of each layer lies in a low-dimensional space.
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.
Extreme Multi-label Classification from Aggregated Labels
Current XMC approaches are not built for such multi-instance multi-label (MIML) training data, and MIML approaches do not scale to XMC sizes.
Learning to Encode Position for Transformer with Continuous Dynamical Model
The main reason is that position information among input units is not inherently encoded, i. e., the models are permutation equivalent; this problem justifies why all of the existing models are accompanied by a sinusoidal encoding/embedding layer at the input.
Ranked #5 on
Semantic Textual Similarity
on MRPC
Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure
The architecture of Transformer is based entirely on self-attention, and has been shown to outperform models that employ recurrence on sequence transduction tasks such as machine translation.
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
In this paper, we propose using Graph DNA, a novel Deep Neighborhood Aware graph encoding algorithm, for exploiting deeper neighborhood information.
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Forecasting high-dimensional time series plays a crucial role in many applications such as demand forecasting and financial predictions.
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
In this paper, we propose AutoAssist, a simple framework to accelerate training of a deep neural network.
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
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.
Learning Word Embeddings for Low-Resource Languages by PU Learning
In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved.
LearningWord Embeddings for Low-resource Languages by PU Learning
In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved.
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
We develop novel regularization schemes and use scalable matrix factorization methods that are eminently suited for high-dimensional time series data that has many missing values.
Asynchronous Parallel Greedy Coordinate Descent
n this paper, we propose and study an Asynchronous parallel Greedy Coordinate Descent (Asy-GCD) algorithm for minimizing a smooth function with bounded constraints.
A Greedy Approach for Budgeted Maximum Inner Product Search
Maximum Inner Product Search (MIPS) is an important task in many machine learning applications such as the prediction phase of a low-rank matrix factorization model for a recommender system.
Extreme Stochastic Variational Inference: Distributed and Asynchronous
Moreover, it requires the parameters to fit in the memory of a single processor; this is problematic when the number of parameters is in billions.
Collaborative Filtering with Graph Information: Consistency and Scalable Methods
Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space.
Ranked #1 on
Recommendation Systems
on Flixster
(using extra training data)
High-dimensional Time Series Prediction with Missing Values
High-dimensional time series prediction is needed in applications as diverse as demand forecasting and climatology.
PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent
In this paper, we parallelize the SDCD algorithms in LIBLINEAR.
A Scalable Asynchronous Distributed Algorithm for Topic Modeling
Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one needs to deal with a large number of topics (typically in the order of thousands).
NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion
One of the key features of NOMAD is that the ownership of a variable is asynchronously transferred between processors in a decentralized fashion.
Distributed, Parallel, and Cluster Computing
Large-scale Multi-label Learning with Missing Labels
The multi-label classification problem has generated significant interest in recent years.
