Paper develops DL methods for signal demodulation in wireless comms.
problem Signal demodulation in wireless communications.
method Proposes DBN-SVM and AdaBoost demodulators using real modulated signals.
result Proposed DBN-SVM and AdaBoost demodulators outperform traditional methods.
POLO simplifies parallel optimization research with a flexible library.
problem Ease of implementing and evaluating parallel optimization algorithms.
method Uses policies and template programming for algorithm decomposition and code reuse.
result Allows implementation of state-of-the-art algorithms in minimal code.
GluonCV and GluonNLP simplify deep learning for CV and NLP.
problem Lack of easy-to-use deep learning tools for CV and NLP.
method Developed modular APIs and pre-trained models for rapid prototyping.
result Facilitates rapid prototyping and reproducible research in CV and NLP.
Horizon is Facebook's open RL platform for large, slow feedback datasets.
problem Training RL models with large, slow feedback datasets.
method End-to-end RL platform with workflows, data preprocessing, distributed training, etc.
result RL models trained with Horizon significantly outperform supervised learning systems.
Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative approach to interpret tree ensemble classifiers by surfacing representative poin…
Paper explores ML demodulation for VLC, presenting three methods and testing accuracy.
problem Improving demodulation accuracy in VLC systems.
method Three ML demodulation methods: CNN, DBN, AdaBoost.
result AdaBoost achieves the best performance among the three methods.
Recent advances in artificial intelligence have been driven by the presence of increasingly realistic and complex simulated environments. However, many of the existing environments provide either unrealistic visuals, inaccurate physics, low task complexity, restricted agent perspective, or a limited capacity for intera…
PTBCC improves accuracy in multi-class annotation aggregation by learning from prototype confusion matrices.
problem Inaccurate and insufficient confusion matrices for annotators in multi-class classification tasks.
method PTBCC (ProtoType learning-driven Bayesian Classifier Combination) uses prototype confusion matrices to capture annotator expertise.
result PTBCC achieves up to 15% accuracy improvement and 3% higher average accuracy compared to existing methods.
ImJoy simplifies deep learning for biomedical research.
problem Computational barriers limit deep learning adoption in biomedical research.
method Open-source browser-based platform for deep learning.
result Facilitates widespread reuse of deep learning solutions.
In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal p…
Vizarel aims to help RL agents by visualizing their performance.
problem Existing visualization tools are not suitable for reinforcement learning.
method Designing a prototype system for interpreting RL agent performance.
result Prototype system identified features for visualizing RL agent performance.
Serverless cloud computing speeds up double machine learning model estimation.
problem Efficiently estimating double machine learning models with minimal cloud resource management.
method Serverless computing with AWS Lambda for repeated cross-fitting.
result Demonstrates significant reduction in estimation times and costs.
ProtoNAM models tabular data with neural networks, making predictions transparent.
problem Tabular data analysis using neural networks lacks transparency and accuracy compared to tree-based methods.
method ProtoNAM introduces prototypes into neural networks to model tabular data while maintaining explainability.
result ProtoNAM outperforms existing NN-based GAMs and provides insights into learned feature patterns.
A monitoring procedure improves machine learning forecasts for digital platforms.
problem Maintaining accurate and stable forecasts for data streams at digital platforms.
method Developed a monitoring procedure to determine when to retrain machine learning algorithms.
result Monitor-based retraining produces accurate forecasts compared to benchmarks.
ProtoryNet interprets text sequences using prototype trajectories for better understanding.
problem Improving text classification interpretability and accuracy.
method ProtoryNet uses prototype trajectories to interpret text sequences, with prototype pruning for better interpretability.
result ProtoryNet outperforms baseline models and reduces performance gap compared to black-box models.
Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents. It is of lower complexity compared to similar 3D platforms (e.g. DeepMind Lab or VizDoom), but emulates physical properties of the real world, such as continuity, multi-modal partially-observable states with…
Prototype networks on hyperspheres improve classification and regression.
problem Improving classification and regression performance.
method Using hyperspherical prototypes for classification and regression, optimizing prototypes through data-independent margin separation.
result Hyperspherical prototype networks outperform other methods in classification, regression, and their combination.
Prototypal analysis is introduced to overcome two shortcomings of archetypal analysis: its sensitivity to outliers and its non-locality, which reduces its applicability as a learning tool. Same as archetypal analysis, prototypal analysis finds prototypes through convex combination of the data points and approximates th…
Infinite mixture prototypes adapt to complex data for few-shot learning.
problem Few-shot learning with complex data distributions.
method Adaptive representation of classes by clusters, inferring cluster number.
result 25% absolute accuracy improvement on alphabets, state-of-the-art semi-supervised clustering.
Optimal prototypes found for challenging pathological geometries.
problem Finding optimal prototypes for pathological geometries is challenging.
method Analytical and heuristic algorithms for finding nearly-optimal prototypes.
result Optimal prototypes can be found analytically for challenging geometries.
Spacematch matches office workers with suitable workspaces based on their preferences.
problem Matching workers with suitable workspaces in ABW environments.
method Developed a web-based mobile app to collect occupant preferences and IoT sensors for real-time environmental feedback.
result Occupant preferences can be segmented and matched to build a recommendation platform.
Method generates prototypes from small datasets for efficient learning.
problem Efficiently learning from small datasets with soft labels.
method Modular method for generating soft-label prototypical lines and Hierarchical Soft-Label Prototype k-Nearest Neighbor algorithm.
result High classification accuracy with significantly fewer prototypes than classes.
Orpheus simplifies deep learning deployment on edge devices.
problem Optimizing deep learning inference on edge devices for efficiency.
method Orpheus is a new framework with a small codebase, minimal dependencies, and easy integration.
result Preliminary results show the effectiveness of Orpheus for inference optimisations.
Paper optimizes hyperspherical prototypes for better class separation.
problem Previous HPL approaches either lack principled optimisation or are limited to one latent dimension.
method Develops a principled optimisation procedure and uses linear block codes to create well-separated prototypes in various dimensions.
result Optimal prototype placement is characterized with achievable and converse bounds, showing near-optimality.
Prototype model improves model auditing and understanding.
problem Auditing and understanding modern language models is expensive and approximate.
method Introduced a sparse, non-negative mixture of learned prototypes trained with clustering objectives.
result Prototype models either surpass or remain within 2.5 percentage points of dense baselines on downstream tasks.
Enhances content moderation with culturally-aware models.
problem Global content moderation policies miss local cultural nuances.
method Fine-tuning encoder-decoder models on media-diet data.
result Improved accuracy in local violation detection and cultural alignment.
IFM improves recommender systems by learning flexible feature interactions.
problem Dealing with feature interactions in recommender systems can degrade performance.
method Introduces Interaction-Aware Mechanism (IAM) to learn feature and field interactions.
result Improves performance on two well-known datasets compared to state-of-the-art methods.
BIDIFAC+ factorizes linked matrices for cancer studies.
problem Integrating multiple omics platforms across various cancer types.
method Flexible approach to simultaneous factorization and decomposition of linked matrices using BIDIFAC+.
result Identifies shared and specific modes of variability across multiple omics platforms and cancer types.
Paper develops brain-inspired unsupervised learning for scalable object detection.
problem Machine vision systems struggle with object discovery and detection, especially under nonideal conditions.
method Leverages large-scale perceptual data to develop brain-inspired flexible, scale, and shift invariant object prototypes.
result Efficient unsupervised learning framework constructs accurate part-aware object models and robust detection algorithms.
The paper uses learned prototypes to explain deep learning models for time-series data.
problem Lack of explainable AI in deep learning models for high-risk decisions.
method Learned prototypes in latent space of deep learning models.
result Prototypes improve classification decisions and provide explainable insights.
TPM improves medical image segmentation by separating foreground and background.
problem Few-shot medical image segmentation challenges due to background variability.
method Tied Prototype Model (TPM) focusing on foreground, adapting thresholds, and using class priors.
result TPM leads to improved segmentation accuracy compared to ADNet.
Smart contracts create digital financial derivatives.
problem Creating a new digital financial derivative contract.
method Applied existing smart contract technologies to develop two prototypes.
result Demonstrated feasibility of digital financial derivatives on centralized and DLT platforms.
A new neural network method improves interpretability and detection of outliers.
problem Improving interpretability and detection of outliers in neural networks.
method Prototype-based learning (PbL) using a winner-take-all (WTA) network with two prototypes: positive and negative.
result The negative prototype is similar to the positive one, aligning with the BCM theory.
We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the "quintessential" observations that best represent clusters in a data…
Shai-am simplifies ML for finance, solving code structure and scalability issues.
problem Challenges in integrating ML for investment strategies, including code structure and scalability.
method Integrates a Python framework with modern open-source technologies to manage containerized pipelines and unified interfaces.
result Facilitates collaborative work in quantitative finance by enhancing reusability and readability.
The recent success of Deep Neural Networks (DNNs) has drastically improved the state of the art for many application domains. While achieving high accuracy performance, deploying state-of-the-art DNNs is a challenge since they typically require billions of expensive arithmetic computations. In addition, DNNs are typica…
Pantypes improve prototypical models by capturing diverse input distributions.
problem Prototypical models lack sufficient data representation in low density regions.
method Introducing pantypes, a sparse set of diverse objects to represent the full diversity of input distribution.
result Pantypes empower prototypical models to foster high diversity, interpretability, and fairness.
Fiber simplifies RL and population-based methods for distributed training.
problem Challenges in RL and population-based methods, including frequent interaction with simulations and dynamic scaling.
method Introducing Fiber, a scalable distributed computing framework.
result Significantly expands accessibility of large-scale parallel computation.
IMKPL learns interpretable prototypes for better classification.
problem Efficient trade-offs between interpretability and prediction accuracy in kernel-based data.
method Local discrimination in feature space, condensed class-homogeneous neighborhoods, combined embedding.
result IMKPL achieves better interpretability and discriminative representation.
A model finds interpretable prototypes for MIL datasets.
problem Finding interpretable prototypes for multiple instance learning.
method Permutation invariant maximally predictive prototype generator.
result The model outperforms existing approaches in accuracy and efficiency.
Classification in the dissimilarity space has become a very active research area since it provides a possibility to learn from data given in the form of pairwise non-metric dissimilarities, which otherwise would be difficult to cope with. The selection of prototypes is a key step for the further creation of the space. …
SPOT uses optimal transport to select important prototypes.
problem Summarizing datasets for better understanding and decision making.
method Modeling prototype selection as a submodular optimization problem and using a greedy algorithm.
result Our approach efficiently selects prototypes with optimal transport that best represent the target dataset.
Deep convolutional neural networks (CNNs) used in practice employ potentially hundreds of layers and 10,000s of nodes. Such network sizes entail significant computational complexity due to the large number of convolutions that need to be carried out; in addition, a large number of parameters needs to be learned and…
We propose a novel architecture for k-shot classification on the Omniglot dataset. Building on prototypical networks, we extend their architecture to what we call Gaussian prototypical networks. Prototypical networks learn a map between images and embedding vectors, and use their clustering for classification. In our…
The condensed nearest neighbor (CNN) algorithm is a heuristic for reducing the number of prototypical points stored by a nearest neighbor classifier, while keeping the classification rule given by the reduced prototypical set consistent with the full set. I present an upper bound on the number of prototypical points ac…
New subspace prototype flag median improves clustering on noisy data.
problem Finding robust prototypes for datasets of images and videos.
method Proposes flag median and introduces FlagIRLS algorithm for its calculation.
result Flag median is robust to outliers and improves cluster purity.
Prototype selection improved using topological data analysis.
problem Improving prototype selection methods for data compression.
method Introducing two topological prototype selector variants: TPS and BoundaryTPS.
result BoundaryTPS achieves the lowest mean Friedman rank on H1 persistence-diagram preservation. DPTA improves CIL by adapting PTMs with dual prototypes.
problem Catastrophic forgetting in incremental learning with pre-trained models.
method Dual-Prototype Network with Task-wise Adaptation (DPTA).
result DPTA consistently outperforms recent methods by 1\%-5\% on multiple benchmarks.