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Found 14 papers, 6 papers with code
AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions
To accelerate therapeutic antibody discovery, computational methods, especially machine learning, have attracted considerable interest for predicting specific interactions between antibody candidates and target antigens such as viruses and bacteria.
Plex: Towards Reliability using Pretrained Large Model Extensions
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures.
Pre-training helps Bayesian optimization too
Contrary to a common belief that BO is suited to optimizing black-box functions, it actually requires domain knowledge on characteristics of those functions to deploy BO successfully.
Ensembling over Classifiers: a Bias-Variance Perspective
Empirically, standard ensembling reducesthe bias, leading us to hypothesize that ensembles of classifiers may perform well in part because of this unexpected reduction. We conclude by an empirical analysis of recent deep learning methods that ensemble over hyperparameters, revealing that these techniques indeed favor bias reduction.
Understanding the bias-variance tradeoff of Bregman divergences
We show that, similarly to the label, the central prediction can be interpreted as the mean of a random variable, where the mean operates in a dual space defined by the loss function itself.
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
In this paper we introduce Uncertainty Baselines: high-quality implementations of standard and state-of-the-art deep learning methods on a variety of tasks.
Population-Based Black-Box Optimization for Biological Sequence Design
The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds of large batches of sequences--a setting that off-the-shelf black-box optimization methods are ill-equipped to handle.
Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling
Casting hyperparameter search as a multi-task Bayesian optimization problem over both hyperparameters and importance sampling design achieves the best of both worlds: by learning a parameterization of IS that trades-off evaluation complexity and quality, we improve upon Bayesian optimization state-of-the-art runtime and final validation error across a variety of datasets and complex neural architectures.
DPPNet: Approximating Determinantal Point Processes with Deep Networks
Determinantal Point Processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items.
Foundations of Sequence-to-Sequence Modeling for Time Series
The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting.
Learning Determinantal Point Processes by Corrective Negative Sampling
To address this issue, which reduces the quality of the learned model, we introduce a novel optimization problem, Contrastive Estimation (CE), which encodes information about "negative" samples into the basic learning model.
Kronecker Determinantal Point Processes
Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of $N$ items.
Diversity Networks: Neural Network Compression Using Determinantal Point Processes
We introduce Divnet, a flexible technique for learning networks with diverse neurons.
Fixed-point algorithms for learning determinantal point processes
Determinantal point processes (DPPs) offer an elegant tool for encoding probabilities over subsets of a ground set.
