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Found 10 papers, 0 papers with code
LLMs for Relational Reasoning: How Far are We?
Large language models (LLMs) have revolutionized many areas (e. g. natural language processing, software engineering, etc.)
Cross-View Graph Consistency Learning for Invariant Graph Representations
In this paper, we propose a cross-view graph consistency learning (CGCL) method that learns invariant graph representations for link prediction.
PST: Improving Quantitative Trading via Program Sketch-based Tuning
Particularly, PST first proposes using a novel symbolic program sketch to embed the abstract human expert knowledge of market trends.
FAIRER: Fairness as Decision Rationale Alignment
Existing fairness regularization terms fail to achieve decision rationale alignment because they only constrain last-layer outputs while ignoring intermediate neuron alignment.
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs.
Unveiling Project-Specific Bias in Neural Code Models
We propose a Cond-Idf measurement to interpret this behavior, which quantifies the relatedness of a token with a label and its project-specificness.
Cross-Lingual Transfer Learning for Statistical Type Inference
Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data.
Measurement methods of radial flow in relativistic heavy-ion collisions
Radial flow can be directly extracted from the azimuthal distribution of mean transverse rapidity.
Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment
Seismic data interpolation based on U-net with texture loss
In this study, a novel Seismic U-net InterpolaTor (SUIT) is proposed to preserve the seismic texture information while reconstructing the missing traces.
Adabot: Fault-Tolerant Java Decompiler
Specifically, in terms of BLEU-4 and Word Error Rate (WER), our performance has reached 94. 50% and 2. 65% on the redundant test set; 92. 30% and 3. 48% on the purified test set.
