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Found 17 papers, 4 papers with code
FastVPINNs: Tensor-Driven Acceleration of VPINNs for Complex Geometries
Variational Physics-Informed Neural Networks (VPINNs) utilize a variational loss function to solve partial differential equations, mirroring Finite Element Analysis techniques.
SpecTr: Fast Speculative Decoding via Optimal Transport
We show that the optimal draft selection algorithm (transport plan) can be computed via linear programming, whose best-known runtime is exponential in $k$.
Treeformer: Dense Gradient Trees for Efficient Attention Computation
Based on such hierarchical navigation, we design Treeformer which can use one of two efficient attention layers -- TF-Attention and TC-Attention.
Teacher Guided Training: An Efficient Framework for Knowledge Transfer
In this paper, we propose the teacher-guided training (TGT) framework for training a high-quality compact model that leverages the knowledge acquired by pretrained generative models, while obviating the need to go through a large volume of data.
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set.
Leveraging redundancy in attention with Reuse Transformers
Pairwise dot product-based attention allows Transformers to exchange information between tokens in an input-dependent way, and is key to their success across diverse applications in language and vision.
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation
State-of-the-art transformer models use pairwise dot-product based self-attention, which comes at a computational cost quadratic in the input sequence length.
Semantic Label Smoothing for Sequence to Sequence Problems
Label smoothing has been shown to be an effective regularization strategy in classification, that prevents overfitting and helps in label de-noising.
Long-tail learning via logit adjustment
Real-world classification problems typically exhibit an imbalanced or long-tailed label distribution, wherein many labels are associated with only a few samples.
Ranked #48 on
Long-tail Learning
on ImageNet-LT
Adversarial robustness via robust low rank representations
Adversarial robustness measures the susceptibility of a classifier to imperceptible perturbations made to the inputs at test time.
DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases
The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set.
A Bottom Up Procedure for Text Line Segmentation of Latin Script
The experimental results show the validity of the procedure.
A Sequential Thinning Algorithm For Multi-Dimensional Binary Patterns
This paper proposes a sequential algorithm that is very easy to understand and modify based on application to perform the thinning of multi-dimensional binary patterns.
Human Shape From Silhouettes Using Generative HKS Descriptors and Cross-Modal Neural Networks
In this work, we present a novel method for capturing human body shape from a single scaled silhouette.
Sparse Local Embeddings for Extreme Multi-label Classification
The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set.
Locally Non-linear Embeddings for Extreme Multi-label Learning
Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hence the effective number of labels can be reduced by projecting the high dimensional label vectors onto a low dimensional linear subspace.
Extreme Multi-Label Classification
General Classification
+2
