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Found 11 papers, 5 papers with code
Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models
Recent advancements in large language models (LLMs) have underscored their importance in the evolution of artificial intelligence.
Variational Flow Models: Flowing in Your Style
We introduce "posterior flows" - generalizations of "probability flows" to a broader class of stochastic processes not necessarily diffusion processes - and propose a systematic training-free method to transform the posterior flow of a "linear" stochastic process characterized by the equation Xt = at * X0 + st * X1 into a straight constant-speed (SC) flow, reminiscent of Rectified Flow.
Domain Generalisation via Risk Distribution Matching
Hence, we propose a compelling proposition: Minimising the divergences between risk distributions across training domains leads to robust invariance for DG.
Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selection
In this paper, we propose to improve the candidate scoring by explicitly incorporating the dependencies between question-context and answer-context into the final representation of a candidate.
Open-Vocabulary Affordance Detection in 3D Point Clouds
In this paper, we present the Open-Vocabulary Affordance Detection (OpenAD) method, which is capable of detecting an unbounded number of affordances in 3D point clouds.
Causal Inference via Style Transfer for Out-of-distribution Generalisation
A well-known existing causal inference method like back-door adjustment cannot be applied to remove spurious correlations as it requires the observation of confounders.
Skin Lesion Recognition with Class-Hierarchy Regularized Hyperbolic Embeddings
Accordingly, the learned prototypes preserve the semantic class relations in the embedding space and we can predict the label of an image by assigning its feature to the nearest hyperbolic class prototype.
Neural Machine Translation of Text from Non-Native Speakers
Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data, especially when the system is trained only on clean data.
Kid on The Phone! Toward Automatic Detection of Children on Mobile Devices
In this work, we present multiple techniques to automatically detect the presence of a child on a smart device, which could be used as the first step on such systems.
Tap-based User Authentication for Smartwatches
This paper presents TapMeIn, an eyes-free, two-factor authentication method for smartwatches.
A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing
Anti-phishing aims to detect phishing content/documents in a pool of textual data.
