For example, a backdoor attack uses one trigger to manipulate the model's outputs on all inputs,

We extend the multinomial logit model to represent some of the empirical phenomena that are frequently observed in the choices made by humans. These phenomena include the similarity effect, the attraction effect, and the compromise effect. We formally quantify the strength of these phenomena that can be represented by our choice model, which illuminates the flexibility of our choice model. We then show that our choice model can be represented as a restricted Boltzmann machine and that its parameters can be learned effectively from data. Our numerical experiments with real data of human choices suggest that we can train our choice model in such a way that it represents the typical phenomena of choice.

Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training.

Neural Information Processing Systems http://nips.cc/

In few-shot learning, a machine learning system learns from a small set of labelled examples relating to a specific task, such that it can generalize to new examples of the same task. Given the limited availability of labelled examples in such tasks, we wish to make use of all the information we can. Usually a model learns task-specific information from a small training-set ( support-set) to predict on an unlabelled validation set ( target-set). The target-set contains additional task-specific information which is not utilized by existing few-shot learning methods. Making use of the target-set examples via transductive learning requires approaches beyond the current methods; at inference time, the target-set contains only unlabelled input data-points, and so discriminative learning cannot be used. In this paper, we propose a framework called Self-Critique and Adapt or SCA, which learns to learn an label-free loss function, parameterized as a neural network. A base-model learns on a support-set using existing methods (e.g.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper proposes to use a deep convolutional neural network for denoising images by generating a lot of noisy and noiseless image pairs using a synthetic blurring process. The proposed method achieves good results on a number of image deblurring tasks. The idea is simple and elegant. It is observed that a 2D deconvolution, which is an inverse of the convolution operator, is itself a 2D convolution operator, albeit with a very large support.

Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise.

We study the relationship between the frequency of a function and the speed at which a neural network learns it. We build on recent results that show that the dynamics of overparameterized neural networks trained with gradient descent can be well approximated by a linear system. When normalized training data is uniformly distributed on a hypersphere, the eigenfunctions of this linear system are spherical harmonic functions.

An EBM is the only object that needs to be trained and designed. Separate networks are not tuned to ensure balance (for example, [He et al., 2019] point out unbalanced training