Search Results for author:
Found 17 papers, 3 papers with code
How does BERT capture semantics? A closer look at polysemous words
The recent paradigm shift to contextual word embeddings has seen tremendous success across a wide range of down-stream tasks.
OpenAssistant Conversations -- Democratizing Large Language Model Alignment
In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations, a human-generated, human-annotated assistant-style conversation corpus consisting of 161, 443 messages in 35 different languages, annotated with 461, 292 quality ratings, resulting in over 10, 000 complete and fully annotated conversation trees.
FIGARO: Generating Symbolic Music with Fine-Grained Artistic Control
Generating music with deep neural networks has been an area of active research in recent years.
Boosting Search Engines with Interactive Agents
This paper presents first successful steps in designing search agents that learn meta-strategies for iterative query refinement in information-seeking tasks.
Generative Minimization Networks: Training GANs Without Competition
Among known problems experienced by practitioners is the lack of convergence guarantees or convergence to a non-optimum cycle.
Rethinking Neural Networks With Benford's Law
To that end, we first define a metric, MLH (Model Enthalpy), that measures the closeness of a set of numbers to Benford's Law and we show empirically that it is a strong predictor of Validation Accuracy.
Meta Answering for Machine Reading
We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment.
Adversarial Training Generalizes Data-dependent Spectral Norm Regularization
We establish a theoretical link between adversarial training and operator norm regularization for deep neural networks.
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
We establish a theoretical link between adversarial training and operator norm regularization for deep neural networks.
Escaping Flat Areas via Function-Preserving Structural Network Modifications
We develop our method for fully-connected as well as convolutional layers.
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
We investigate conditions under which test statistics exist that can reliably detect examples, which have been adversarially manipulated in a white-box attack.
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels
Black-Box attacks on machine learning models occur when an attacker, despite having no access to the inner workings of a model, can successfully craft an attack by means of model theft.
Parametrizing filters of a CNN with a GAN
It is commonly agreed that the use of relevant invariances as a good statistical bias is important in machine-learning.
Flexible Prior Distributions for Deep Generative Models
We consider the problem of training generative models with deep neural networks as generators, i. e. to map latent codes to data points.
Semantic Interpolation in Implicit Models
In implicit models, one often interpolates between sampled points in latent space.
Generator Reversal
We consider the problem of training generative models with deep neural networks as generators, i. e. to map latent codes to data points.
Scalable Adaptive Stochastic Optimization Using Random Projections
We show that the regret of Ada-LR is close to the regret of full-matrix AdaGrad which can have an up-to exponentially smaller dependence on the dimension than the diagonal variant.
