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Found 27 papers, 7 papers with code
Faster graphical model identification of tandem mass spectra using peptide word lattices
Liquid chromatography coupled with tandem mass spectrometry, also known as shotgun proteomics, is a widely-used high-throughput technology for identifying proteins in complex biological samples.
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation
While the robust versions have been studied in the theory community, existing work has focused on tight approximation guarantees, and the resultant algorithms are not, in general, scalable to very large real-world applications.
Blending LSTMs into CNNs
We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications
In the present paper, we bridge this gap, by proposing several new algorithms (including greedy, majorization-minimization, minorization-maximization, and relaxation algorithms) that not only scale to large datasets but that also achieve theoretical approximation guarantees comparable to the state-of-the-art.
Diverse Ensemble Evolution: Curriculum Data-Model Marriage
We study a new method (``Diverse Ensemble Evolution (DivE$^2$)'') to train an ensemble of machine learning models that assigns data to models at each training epoch based on each model's current expertise and an intra- and inter-model diversity reward.
Jumpout: Improved Dropout for Deep Neural Networks with Rectified Linear Units
In this paper, we discuss three novel observations about dropout to better understand the generalization of DNNs with rectified linear unit (ReLU) activations: 1) dropout is a smoothing technique that encourages each local linear model of a DNN to be trained on data points from nearby regions; 2) a constant dropout rate can result in effective neural-deactivation rates that are significantly different for layers with different fractions of activated neurons; and 3) the rescaling factor of dropout causes an inconsistency to occur between the normalization during training and testing conditions when batch normalization is also used.
Bias Also Matters: Bias Attribution for Deep Neural Network Explanation
In particular, we study how to attribute a DNN's bias to its input features.
Dynamic Instance Hardness
The advantages of DIHCL, compared to other curriculum learning approaches, are: (1) DIHCL does not require additional inference steps over the data not selected by DIHCL in each epoch, (2) the dynamic instance hardness, compared to static instance hardness (e. g., instantaneous loss), is more stable as it integrates information over the entire training history up to the present time.
Curriculum Learning by Dynamic Instance Hardness
Compared to existing CL methods: (1) DIH is more stable over time than using only instantaneous hardness, which is noisy due to stochastic training and DNN's non-smoothness; (2) DIHCL is computationally inexpensive since it uses only a byproduct of back-propagation and thus does not require extra inference.
Robust Curriculum Learning: from clean label detection to noisy label self-correction
Neural nets training can easily overfit to noisy labels and end with poor generalization performance.
Exploring the representativeness of the M5 competition data
The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field in order to identify best practices and highlight their practical implications.
Constrained Robust Submodular Partitioning
Robust submodular partitioning promotes the diversity of every block in the partition.
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
In this study, we propose the Bimanual Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two dexterous hands with tens of bimanual manipulation tasks and thousands of target objects.
A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative Object
Module I realizes the multi-target trajectory planning for two end-effectors within a large target space.
Reinforcement Learning with Prior Policy Guidance for Motion Planning of Dual-Arm Free-Floating Space Robot
However, due to the increase in planning dimension and the intensification of system dynamics coupling, the motion planning of dual-arm free-floating space robots remains an open challenge.
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy.
A Policy Optimization Method Towards Optimal-time Stability
In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization.
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization
In the early stage, our method loosens the practical constraints of unsafe transitions (adding extra safety budget) with the aid of a new metric we propose.
A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation
Symmetric bi-manual manipulation is essential for various on-orbit operations due to its potent load capacity.
IMAP: Intrinsically Motivated Adversarial Policy
Our experiments validate the effectiveness of the four types of adversarial intrinsic regularizers and BR in enhancing black-box adversarial policy learning across a variety of environments.
Machine Learning Force Fields with Data Cost Aware Training
To address this issue, we propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
Foundation Reinforcement Learning: towards Embodied Generalist Agents with Foundation Prior Assistance
Our method tolerates the unavoidable noise in embodied foundation models.
DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands
Achieving human-like dexterous manipulation remains a crucial area of research in robotics.
Diffusion Cocktail: Fused Generation from Diffusion Models
In this work, we study the combinations of diffusion models.
EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
We have expanded the performance of EfficientZero to multiple domains, encompassing both continuous and discrete actions, as well as visual and low-dimensional inputs.
Time-Consistent Self-Supervision for Semi-Supervised Learning
In this paper, we study the dynamics of neural net outputs in SSL and show that selecting and using first the unlabeled samples with more consistent outputs over the course of training (i. e., "time-consistency") can improve the final test accuracy and save computation.
