Papers Rotated MNIST
“Rotated MNIST” 태그가 달린 논문 51편 · 필터 해제
Optimal Training-Time Scaling in Gradual Adaptation
In gradual adaptation, how should the training time on each task change as the number of intermediate tasks increases? We study this question for overparameterized linear regression tasks that change smoothly and share a…
Rotated MNISTLearning Coherent Representations: A Topological Approach to Interpretability
Deep neural networks learn representations where individual features often lack interpretable meaning; a single neuron may activate for scattered, unrelated inputs. We introduce coherence, a geometric property inspired b…
Rotated MNISTComputing a Characteristic Orientation for Rotation-Independent Image Analysis
Handling geometric transformations, particularly rotations, remains a challenge in deep learning for computer vision. Standard neural networks lack inherent rotation invariance and typically rely on data augmentation or …
Data AugmentationRotated MNISTDopamine: Brain Modes, Not Brains
Parameter-efficient fine-tuning (PEFT) methods such as \lora{} adapt large pretrained models by adding small weight-space updates. While effective, weight deltas are hard to interpret mechanistically, and they do not dir…
parameter-efficient fine-tuningRotated MNISTDomain Expansion: A Latent Space Construction Framework for Multi-Task Learning
Training a single network with multiple objectives often leads to conflicting gradients that degrade shared representations, forcing them into a compromised state that is suboptimal for any single task--a problem we term…
Multi-Task LearningGaze EstimationRotated MNISTMitigating Catastrophic Forgetting in Streaming Generative and Predictive Learning via Stateful Replay
Many deployed learning systems must update models on streaming data under memory constraints. The default strategy, sequential fine-tuning on each new phase, is architecture-agnostic but often suffers catastrophic forget…
Time Series ForecastingContinual LearningRotated MNISTEquivariant-Aware Structured Pruning for Efficient Edge Deployment: A Comprehensive Framework with Adaptive Fine-Tuning
This paper presents a novel framework combining group equivariant convolutional neural networks (G-CNNs) with equivariant-aware structured pruning to produce compact, transformation-invariant models for resource-constrai…
Knowledge DistillationRotated MNISTSelf-Training with Dynamic Weighting for Robust Gradual Domain Adaptation
In this paper, we propose a new method called Self-Training with Dynamic Weighting (STDW), which aims to enhance robustness in Gradual Domain Adaptation (GDA) by addressing the challenge of smooth knowledge migration fro…
Domain AdaptationRotated MNISTReinforced Domain Selection for Continuous Domain Adaptation
Continuous Domain Adaptation (CDA) effectively bridges significant domain shifts by progressively adapting from the source domain through intermediate domains to the target domain. However, selecting intermediate domains…
Reinforcement LearningDomain AdaptationRotated MNISTONG: Orthogonal Natural Gradient Descent
Orthogonal Gradient Descent (OGD) has emerged as a powerful method for continual learning. However, its Euclidean projections do not leverage the underlying information-geometric structure of the problem, which can lead …
Continual LearningRotated MNISTWhat's Inside Your Diffusion Model? A Score-Based Riemannian Metric to Explore the Data Manifold
Recent advances in diffusion models have demonstrated their remarkable ability to capture complex image distributions, but the geometric properties of the learned data manifold remain poorly understood. We address this g…
NavigateRotated MNISTGDO: Gradual Domain Osmosis
In this paper, we propose a new method called Gradual Domain Osmosis, which aims to solve the problem of smooth knowledge migration from source domain to target domain in Gradual Domain Adaptation (GDA). Traditional Grad…
Domain AdaptationRotated MNISTOn the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory
Symmetries (transformations by group actions) are present in many datasets, and leveraging them holds significant promise for improving predictions in machine learning. In this work, we aim to understand when and how dee…
Rotated MNISTUnsupervised discovery of the shared and private geometry in multi-view data
Modern applications often leverage multiple views of a subject of study. Within neuroscience, there is growing interest in large-scale simultaneous recordings across multiple brain regions. Understanding the relationship…
HippocampusRotated MNISTContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encoding
Normalizing flow-based generative models have been widely used in applications where the exact density estimation is of major importance. Recent research proposes numerous methods to improve their expressivity. However, …
Anomaly DetectionDensity EstimationGeneral KnowledgeRotated MNIST+2Progressive Conservative Adaptation for Evolving Target Domains
Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. However, in many real-world cases, target data usually emerge sequentially and have continuously evolving d…
Domain AdaptationMeta-LearningRotated MNISTScale-Rotation-Equivariant Lie Group Convolution Neural Networks (Lie Group-CNNs)
The weight-sharing mechanism of convolutional kernels ensures translation-equivariance of convolution neural networks (CNNs). Recently, rotation-equivariance has been investigated. However, research on scale-equivariance…
image-classificationImage ClassificationRotated MNISTGroup Invariant Global Pooling
Much work has been devoted to devising architectures that build group-equivariant representations, while invariance is often induced using simple global pooling mechanisms. Little work has been done on creating expressiv…
Rotated MNISTArtificial Neuronal Ensembles with Learned Context Dependent Gating
Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for se…
Continual LearningPermuted-MNISTRotated MNISTLearning unfolded networks with a cyclic group structure
Deep neural networks lack straightforward ways to incorporate domain knowledge and are notoriously considered black boxes. Prior works attempted to inject domain knowledge into architectures implicitly through data augme…
Data AugmentationRotated MNIST