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Found 33 papers, 22 papers with code
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition
We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and initiate discussions on formal and robust evaluation methodologies.
Data Selection for Transfer Unlearning
In this work, we advocate for a relaxed definition of unlearning that does not address privacy applications but targets a scenario where a data owner withdraws permission of use of their data for training purposes.
Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments.
BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics
The ability for a machine learning model to cope with differences in training and deployment conditions--e. g. in the presence of distribution shift or the generalization to new classes altogether--is crucial for real-world use cases.
A density estimation perspective on learning from pairwise human preferences
Learning from human feedback (LHF) -- and in particular learning from pairwise preferences -- has recently become a crucial ingredient in training large language models (LLMs), and has been the subject of much research.
Active learning for fast and slow modeling attacks on Arbiter PUFs
In most modeling attacks, a random subset of challenge-response-pairs (CRPs) are used as the labeled data for the machine learning algorithm.
In Search for a Generalizable Method for Source Free Domain Adaptation
Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data.
Proper Reuse of Image Classification Features Improves Object Detection
A common practice in transfer learning is to initialize the downstream model weights by pre-training on a data-abundant upstream task.
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
We propose a method, Head-to-Toe probing (Head2Toe), that selects features from all layers of the source model to train a classification head for the target-domain.
Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization
We propose a method, Head-to-Toe probing (Head2Toe), that selects features from all layers of the source model to train a classification head for the target-domain.
Composing Features: Compositional Model Augmentation for Steerability of Music Transformers
To tackle the combinatorial nature of composing features, we propose a compositional approach to steering music transformers, building on lightweight fine-tuning methods such as prefix tuning and bias tuning.
Impact of Aliasing on Generalization in Deep Convolutional Networks
We investigate the impact of aliasing on generalization in Deep Convolutional Networks and show that data augmentation schemes alone are unable to prevent it due to structural limitations in widely used architectures.
Domain Conditional Predictors for Domain Adaptation
Domain adaptation approaches thus appeared as a useful framework yielding extra flexibility in that distinct train and test data distributions are supported, provided that other assumptions are satisfied such as covariate shift, which expects the conditional distributions over labels to be independent of the underlying data distribution.
Learning a Universal Template for Few-shot Dataset Generalization
Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purpose of training an adaptable model that can then learn classes from new datasets using only a few examples.
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark
To bridge this gap, we perform a cross-family study of the best transfer and meta learners on both a large-scale meta-learning benchmark (Meta-Dataset, MD), and a transfer learning benchmark (Visual Task Adaptation Benchmark, VTAB).
Learning Flexible Classifiers with Shot-CONditional Episodic (SCONE) Training
We discover that fine-tuning on episodes of a particular shot can specialize the pre-trained model to solving episodes of that shot at the expense of performance on other shots, in agreement with a trade-off recently observed in the context of end-to-end episodic training.
An Effective Anti-Aliasing Approach for Residual Networks
Image pre-processing in the frequency domain has traditionally played a vital role in computer vision and was even part of the standard pipeline in the early days of deep learning.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Few-shot classification refers to learning a classifier for new classes given only a few examples.
Ranked #7 on
Few-Shot Image Classification
on Meta-Dataset Rank
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making.
Cross-Modulation Networks for Few-Shot Learning
A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples.
Generative Adversarial Networks: An Overview
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data.
FiLM: Visual Reasoning with a General Conditioning Layer
We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation.
Ranked #3 on
Visual Question Answering (VQA)
on CLEVR-Humans
Image Retrieval with Multi-Modal Query
Visual Question Answering (VQA)
+1
Learning Visual Reasoning Without Strong Priors
Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.
Exploring the structure of a real-time, arbitrary neural artistic stylization network
In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair.
Improved Training of Wasserstein GANs
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.
Ranked #3 on
Image Generation
on CAT 256x256
A Learned Representation For Artistic Style
In this work we investigate the construction of a single, scalable deep network that can parsimoniously capture the artistic style of a diversity of paintings.
Adversarially Learned Inference
We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
A guide to convolution arithmetic for deep learning
We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures.
Discriminative Regularization for Generative Models
We propose to take advantage of this by using the representations from discriminative classifiers to augment the objective function corresponding to a generative model.
Blocks and Fuel: Frameworks for deep learning
We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel.
On the Challenges of Physical Implementations of RBMs
Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC.
