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Found 31 papers, 12 papers with code
SCANNER: Knowledge-Enhanced Approach for Robust Multi-modal Named Entity Recognition of Unseen Entities
Our approach demonstrates competitive performance on the NER benchmark and surpasses existing methods on both MNER and GMNER benchmarks.
Multi-modal Named Entity Recognition
named-entity-recognition
+2
Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity
Recent advances in text-guided image compression have shown great potential to enhance the perceptual quality of reconstructed images.
Attention-aware Semantic Communications for Collaborative Inference
In our framework, the lightweight ViT model on the edge device acts as a semantic encoder, efficiently identifying and selecting the crucial image information required for the classification task.
Hybrid Neural Representations for Spherical Data
In this paper, we study hybrid neural representations for spherical data, a domain of increasing relevance in scientific research.
In Search of a Data Transformation That Accelerates Neural Field Training
Neural field is an emerging paradigm in data representation that trains a neural network to approximate the given signal.
Data-driven System Interconnections and a Novel Data-enabled Internal Model Control
Over the past two decades, there has been a growing interest in control systems research to transition from model-based methods to data-driven approaches.
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression
In the second strategy, the non-dropped intermediate feature and gradient vectors are quantized using adaptive quantization levels determined based on the ranges of the vectors.
Debiased Distillation by Transplanting the Last Layer
Deep models are susceptible to learning spurious correlations, even during the post-processing.
MaskedKD: Efficient Distillation of Vision Transformers with Masked Images
We present MaskedKD, a simple yet effective strategy that can significantly reduce the cost of distilling ViTs without sacrificing the prediction accuracy of the student model.
Discovering and Mitigating Visual Biases through Keyword Explanation
The keyword explanation form of visual bias offers several advantages, such as a clear group naming for bias discovery and a natural extension for debiasing using these group names.
Modality-Agnostic Variational Compression of Implicit Neural Representations
We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR).
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
(a) Fairness Intervention (FI): emphasize the minority samples that are hard to generate due to the spurious correlation in the training dataset.
Data-Driven Inverse of Linear Systems and Application to Disturbance Observers
Specifically, the problem addressed here is to find an input sequence from the corresponding output sequence based on pre-collected input and output data.
Scalable Neural Video Representations with Learnable Positional Features
Succinct representation of complex signals using coordinate-based neural representations (CNRs) has seen great progress, and several recent efforts focus on extending them for handling videos.
Meta-Learning with Self-Improving Momentum Target
The idea of using a separately trained target model (or teacher) to improve the performance of the student model has been increasingly popular in various machine learning domains, and meta-learning is no exception; a recent discovery shows that utilizing task-wise target models can significantly boost the generalization performance.
Few-Shot Unlearning by Model Inversion
We consider a practical scenario of machine unlearning to erase a target dataset, which causes unexpected behavior from the trained model.
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation
The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes.
Zero-shot Blind Image Denoising via Implicit Neural Representations
Recent denoising algorithms based on the "blind-spot" strategy show impressive blind image denoising performances, without utilizing any external dataset.
Meta-Learning Sparse Implicit Neural Representations
Implicit neural representations are a promising new avenue of representing general signals by learning a continuous function that, parameterized as a neural network, maps the domain of a signal to its codomain; the mapping from spatial coordinates of an image to its pixel values, for example.
Co$^2$L: Contrastive Continual Learning
Recent breakthroughs in self-supervised learning show that such algorithms learn visual representations that can be transferred better to unseen tasks than joint-training methods relying on task-specific supervision.
Co2L: Contrastive Continual Learning
Recent breakthroughs in self-supervised learning show that such algorithms learn visual representations that can be transferred better to unseen tasks than cross-entropy based methods which rely on task-specific supervision.
MASKER: Masked Keyword Regularization for Reliable Text Classification
We claim that one central obstacle to the reliability is the over-reliance of the model on a limited number of keywords, instead of looking at the whole context.
Learning from Failure: De-biasing Classifier from Biased Classifier
Neural networks often learn to make predictions that overly rely on spurious corre- lation existing in the dataset, which causes the model to be biased.
Provable Memorization via Deep Neural Networks using Sub-linear Parameters
It is known that $O(N)$ parameters are sufficient for neural networks to memorize arbitrary $N$ input-label pairs.
Layer-adaptive sparsity for the Magnitude-based Pruning
Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance.
Learning from Failure: Training Debiased Classifier from Biased Classifier
Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Minimum Width for Universal Approximation
In this work, we provide the first definitive result in this direction for networks using the ReLU activation functions: The minimum width required for the universal approximation of the $L^p$ functions is exactly $\max\{d_x+1, d_y\}$.
Learning Bounds for Risk-sensitive Learning
The second result, based on a novel variance-based characterization of OCE, gives an expected loss guarantee with a suppressed dependence on the smoothness of the selected OCE.
Lookahead: a Far-Sighted Alternative of Magnitude-based Pruning
Magnitude-based pruning is one of the simplest methods for pruning neural networks.
Learning finite-dimensional coding schemes with nonlinear reconstruction maps
This paper generalizes the Maurer--Pontil framework of finite-dimensional lossy coding schemes to the setting where a high-dimensional random vector is mapped to an element of a compact set of latent representations in a lower-dimensional Euclidean space, and the reconstruction map belongs to a given class of nonlinear maps.
Minimax Statistical Learning with Wasserstein Distances
As opposed to standard empirical risk minimization (ERM), distributionally robust optimization aims to minimize the worst-case risk over a larger ambiguity set containing the original empirical distribution of the training data.
