Biological sequence nearest neighbor search plays a fundamental role in bioinformatics. To alleviate the pain of quadratic complexity for conventional distance computation, neural distance embeddings, which project sequences into geometric space, have been recognized as a promising paradigm. To maintain the distance order between sequences, these models all deploy triplet loss and use intuitive methods to select a subset of triplets for training from a vast selection space. However, we observed that such training often enables models to distinguish only a fraction of distance orders, leaving others unrecognized. Moreover, naively selecting more triplets for training under the state-of-the-art network not only adds costs but also hampers model performance. In this paper, we introduce Bio-kNN: a kNN search framework for biological sequences. It includes a systematic triplet selection method and a multi-head network, enhancing the discernment of all distance orders without increasing training expenses. Initially, we propose a clustering-based approach to partition all triplets into several clusters with similar properties, and then select triplets from these clusters using an innovative strategy. Meanwhile, we noticed that simultaneously training different types of triplets in the same network cannot achieve the expected performance, thus we propose a multi-head network to tackle this. Our network employs a convolutional neural network(CNN) to extract local features shared by all clusters, and then learns a multi-layer perception(MLP) head for each cluster separately. Besides, we treat CNN as a special head, thereby integrating crucial local features which are neglected in previous models into our model for similarity recognition. Extensive experiments show that our Bio-kNN significantly outperforms the state-of-the-art methods on two large-scale datasets without increasing the training cost.

With breakthroughs in pretrained language models, a large number of finetuned models specialized in distinct domains have surfaced online. Yet, when faced with a fresh dataset covering multiple (sub)domains, their performance might degrade. Reusing these available finetuned models to train a new model is a more feasible solution than the finetuning method that demands extensive manual labeling. Knowledge Amalgamation (KA) is such a model reusing technique, which derives a new model (termed student model) by amalgamating those trained models (termed teacher models) tailored for distinct domains, bypassing the need for manual labeling. However, when the domains of text samples are unknown, selecting a number of appropriate teacher models (simply called a combination) for reuse becomes complicated. To learn an accurate student model, the classical KA method resorts to manual selections, a process both tedious and inefficient. Our study pioneers the automation of this combination selection process for KA in the fundamental text classification task, an area previously unexplored.

Similarity search is a fundamental but expensive operator in querying trajectory data, due to its quadratic complexity of distance computation. To mitigate the computational burden for long trajectories, neural networks have been widely employed for similarity learning and each trajectory is encoded as a high-dimensional vector for similarity search with linear complexity. Given the sequential nature of trajectory data, previous efforts have been primarily devoted to the utilization of RNNs or Transformers. In this paper, we argue that the common practice of treating trajectory as sequential data results in excessive attention to capturing long-term global dependency between two sequences. Instead, our investigation reveals the pivotal role of local similarity, prompting a revisit of simple CNNs for trajectory similarity learning. We introduce ConvTraj, incorporating both 1D and 2D convolutions to capture sequential and geo-distribution features of trajectories, respectively. In addition, we conduct a series of theoretical analyses to justify the effectiveness of ConvTraj. Experimental results on four real-world large-scale datasets demonstrate that ConvTraj achieves state-of-the-art accuracy in trajectory similarity search. Owing to the simple network structure of ConvTraj, the training and inference speed on the Porto dataset with 1.6 million trajectories are increased by at least 240x and 2.16x, respectively.

In recent years, there has been increasing interest in adopting published neural retrieval models learned from corpora for text retrieval. Although these models achieve excellent retrieval performance, in terms of popular accuracy metrics, on datasets they have been trained, their performance on new text data might degrade. To obtain the desired retrieval performance on both the data used in training and the latest data collected after training, the simple approach of learning a new model from both datasets is not always feasible since the annotated dataset used in training is often not published along with the learned model. Knowledge amalgamation (KA) is an emerging technique to deal with this problem of inaccessibility of data used in previous training. KA learns a new model (called a student model) from new data by reusing (called amalgamating) a number of trained models (called teacher models) instead of accessing the teachers' original training data. However, in order to efficiently learn an accurate student model, the classical KA approach requires manual selection of an appropriate subset of teacher models for amalgamation. This manual procedure for selecting teacher models prevents the classical KA from being scaled to retrieval tasks for which a large number of candidate teacher models are ready to be reused.