Search Results for author:
Found 12 papers, 8 papers with code
ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms
Our algorithms are deterministic and achieve high scalability across a diverse set of challenging datasets.
OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries
State-of-the-art algorithms for Approximate Nearest Neighbor Search (ANNS) such as DiskANN, FAISS-IVF, and HNSW build data dependent indices that offer substantially better accuracy and search efficiency over data-agnostic indices by overfitting to the index data distribution.
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
The outcome of the competition was ranked leaderboards of algorithms in each track based on recall at a query throughput threshold.
FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search
Approximate nearest neighbor search (ANNS) is a fundamental building block in information retrieval with graph-based indices being the current state-of-the-art and widely used in the industry.
DROCC: Deep Robust One-Class Classification
Classical approaches for one-class problems such as one-class SVM and isolation forest require careful feature engineering when applied to structured domains like images.
Ranked #3 on
Anomaly Detection
on UEA time-series datasets
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Standard Convolutional Neural Networks (CNNs) designed for computer vision tasks tend to have large intermediate activation maps.
Ranked #27 on
Face Detection
on WIDER Face (Medium)
Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node
We present a new graph-based indexing and search system called DiskANN that can index, store, and search a billion point database on a single workstation with just 64GB RAM and an inexpensive solid-state drive (SSD).
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices
The second layer consumes the output of the first layer using a second RNN thus capturing long dependencies.
GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users
Our in-lab study shows that GesturePod achieves 92% gesture recognition accuracy and can help perform common smartphone tasks faster.
Ranked #1 on
Gesture Recognition
on GesturePod
Word2Sense: Sparse Interpretable Word Embeddings
We present an unsupervised method to generate Word2Sense word embeddings that are interpretable {---} each dimension of the embedding space corresponds to a fine-grained sense, and the non-negative value of the embedding along the j-th dimension represents the relevance of the j-th sense to the word.
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices
We propose a method, EMI-RNN, that exploits these observations by using a multiple instance learning formulation along with an early prediction technique to learn a model that achieves better accuracy compared to baseline models, while simultaneously reducing computation by a large fraction.
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.
