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Found 11 papers, 3 papers with code
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning
We consider a different lower bound on the MI consisting of an entropy and a reconstruction term (ER), and analyze the main MVSSL families through its lens.
DUET: 2D Structured and Approximately Equivariant Representations
We propose 2D strUctured and EquivarianT representations (coined DUET), which are 2d representations organized in a matrix structure, and equivariant with respect to transformations acting on the input data.
Homomorphic Self-Supervised Learning
In this work, we observe that many existing self-supervised learning algorithms can be both unified and generalized when seen through the lens of equivariant representations.
Symphony: Composing Interactive Interfaces for Machine Learning
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems.
Fair SA: Sensitivity Analysis for Fairness in Face Recognition
One such high impact domain is that of face recognition, with real world applications involving images affected by various degradations, such as motion blur or high exposure.
Challenges of Adversarial Image Augmentations
Image augmentations applied during training are crucial for the generalization performance of image classifiers.
Stochastic Contrastive Learning
While state-of-the-art contrastive Self-Supervised Learning (SSL) models produce results competitive with their supervised counterparts, they lack the ability to infer latent variables.
Self-conditioning pre-trained language models
We compare our method with FUDGE and PPLM-BoW, and show that our approach is able to achieve gender parity at a lower perplexity.
Finding Experts in Transformer Models
We show that expert units are important in several ways: (1) The presence of expert units is correlated ($r^2=0. 833$) with the generalization power of TM, which allows ranking TM without requiring fine-tuning on suites of downstream tasks.
NETWORK COMPRESSION USING CORRELATION ANALYSIS OF LAYER RESPONSES
Principal Filter Analysis (PFA) is an easy to implement, yet effective method for neural network compression.
Filter Distillation for Network Compression
We propose two algorithms: the first allows users to target compression to specific network property, such as number of trainable variable (footprint), and produces a compressed model that satisfies the requested property while preserving the maximum amount of spectral energy in the responses of each layer, while the second is a parameter-free heuristic that selects the compression used at each layer by trying to mimic an ideal set of uncorrelated responses.
